Climate transition risks arise during the global transition to a low-carbon economy. These risks encompass four principal subcategories: legal and policy, technological, market, and reputational. This study reviews climate transition risks for Lithuania's economic sectors and evaluates them by using the methodology developed by the authors of this work. In this study, non-financial enterprises were analysed. Six economic sector types (NACE branches) with the highest turnover in 2022 were selected for analysis: (1) manufacturing, (2) wholesale and retail trade, (3) electricity, gas, steam, and air conditioning supply, (4) information and communication, (5) construction, and (6) transportation and storage. For each selected economic sector type, thirty companies with the highest revenues in 2021 were selected for analysis. This study analysed selected companies' strategies for reducing greenhouse gas (GHG) emissions by 2030 and 2050, alongside evaluating their GHG reports across three scopes. Furthermore, financial metrics (debt-to-equity ratio and return on assets), as well as organization structures were considered. Based on the results of a study by the European Central Bank (ECB/ESRB, 2021), each economic sector type was assigned a score that assesses the long-term impact of a rapid transition from fossil fuels. Transition risks were categorized into five tiers: very high, high, moderate, low, or very low, and subsequently attributed to the analysed companies. The results are presented by aggregating the risks according to NACE branches. Obtained results indicate that in Lithuania, a very high transitional risk arises for companies engaged in transportation and storage, as well as construction activities. It was found that Lithuania's companies engaged in construction activities most often do not have climate and energy strategies for reducing GHG emissions by 2030 and 2050, and neglecting potential climate change-related risks. High risk was identified for Lithuanian companies engaged in wholesale and retail trade, and energy supply companies. The low risk was identified for companies engaged in information and communication activities. The financial results of information and communication companies are good, with many of them having 2030 and 2050 climate and energy strategies and the climate transition risk long-term impact for them is low as well. The conducted research is important as an early warning indicator of existing climate transition risks and the increasing need to adequately prepare for them.

How to cite: Kapilovaite, J. and Daugvila, D.: Climate Transition Risks for Lithuania’s Economy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-347, https://doi.org/10.5194/ems2024-347, 2024.

It is important to determine the distribution of indoor and outdoor populations when assessing the risk of extreme summer temperatures in urban areas, the heat island effect, air pollution, and disasters. However, conventional indoor/outdoor determination using sensors and survey forms has problems in scale and immediacy, and it is difficult to determine the population distribution by time of day and by subregion. This study developed and verified a new estimation method using location-based service data with the aim of accurately estimating the distribution of indoor and outdoor population in urban areas and clarifying its temporal and spatial variation in some wards of Tokyo Metropolis, Japan.

The research methodology used point-type data from the global positioning system (GPS) communication, grid-type data acquired from mobile phone base stations, and building polygon data, and combined them to estimate the indoor and outdoor population by time and grid. Specifically, the grid-type demographics were used to recover confidential population data from point-type location service data, which were superimposed on building polygons and determined the indoor/outdoor population based on travel distances. The estimated indoor population was compared and validated with the person-trip survey (PT survey) data at the grid level.

As a result, validation with the PT survey in Nerima Ward confirmed that the estimated behavior of the indoor population, including confidential data restoration, was close to the actual behavior. The model is capable of capturing indoor and outdoor population movements by time of day and by grid, and its results are expected to have a wide range of social applications in the future, including risk assessment for heat stroke, air pollution, and disasters.

How to cite: Kimura, T., Suzuki, T., Ohashi, Y., Takane, Y., Yamaguchi, K., and Ihara, T.: Estimation of Distribution of Indoor and Outdoor Population Using Location-Based Serviced Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-222, https://doi.org/10.5194/ems2024-222, 2024.

As global temperatures rise, there is a significant increase in the demand for cooling energy in buildings, leading to heightened attention on urban energy demands. The direct impact of cooling demand on urban energy burdens, coupled with a growing body of literature on energy, poverty, and other social factors, has underscored the importance of this issue. This study focused on exploring the relationship between Taiwan's regional climate conditions in high-temperature environments, social factors in geographical context, and energy burdens. The objective was to analyze the distribution of energy burdens affected by thermal shocks and social vulnerability, identifying vulnerable areas to facilitate climate change adaptation strategies.

Covering 20 counties and cities in Taiwan, this study employed administrative divisions as analytical units to discuss the distribution of energy burdens in Taiwan in 2018 and 2023. Regarding climatic factors, this study adopted the dynamical downscaling data produced by National Science and Technology Center for Disaster Reduction (NCDR). The data included the changes in energy demand under the current and IPCC-defined RCP8.5 warming scenarios. Additionally, the study examined the influence of social factors such as population structure, socioeconomics, and social vulnerability.

The results reveal variations in the distribution of energy burdens in 2023, ranging from a minimum of 0.2% to a maximum of 30%. Over half of the counties and cities have energy burdens concentrated between 5.4% and 7.9%. Cooling demand in Taiwan is highest in July, followed by August. Therefore, the high energy burden is reflected in the months of August and September. In the climate change scenario, the demand for cooling increases by up to 26.3% in August. The spatial distribution shows that energy burdens are higher in mountainous and suburban areas, despite lower cooling demand there. Conversely, high cooling demand is concentrated in major urban centers, where energy burdens are lower. Finally, the study integrates distribution maps of cooling demand and other social factors to explore the relationship between energy burdens and climate, population, and society.

Keywords: Energy Burden; Cooling Degree Hours; Spatial Analysis; Climate Change

How to cite: Chang, C. and Lin, T.-P.: The impacts of energy burden and social vulnerability in Taiwan under climate change and urban heat island, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-293, https://doi.org/10.5194/ems2024-293, 2024.

In the last decades, climate-related extremes are becoming a recurrent feature in many hotspot regions, as southern Latin America, the Mediterranean, the sub-Saharan Africa, north-eastern China, and southern Australia. Depending on the type of the extreme, reported damages of single events – often on specific sectors – are becoming available with increasing detail, but this is limited to recent years and does not apply to all countries. Consequently, to estimate the socio-economic impacts of climate change, the usual approach is to regress climate variables versus indicators as the Gross Domestic Product (GDP), resulting in annual temperature being the major or the only driver for most sectors, with heterogeneous marginal influence of precipitation. In this presentation, we focus on the benefit of including climate extremes in such models, specifically – as single or compound events – heatwaves, cold waves, rainfall extremes, droughts, and snowfall. Using daily and high-resolution climate data and sub-national socioeconomic indicators over the past decades, we explore the role of the lagged effect between climate extremes and the economic response, represented by GDP, but also other indicators as the Human Development Index. To include the climate extremes in damage functions, a parameterization for such lagged effects is needed, but its explicit modelling is not free from uncertainties, which can be very large, to the point that they might suggest excluding some extremes over specific areas. We also present – in preliminary form – an SSP-based estimation of future impacts of climate extremes, using a set of different global damage functions, also tentatively including regional aggregation for specific climate extremes.

How to cite: Mastropietro, M., Chiani, L., Ghirri, J., Rodriguez-Pardo, C., Spinoni, J., and Tavoni, M.: Evaluating the role of time lag between climate extremes and socioeconomic impacts, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-557, https://doi.org/10.5194/ems2024-557, 2024.

The aviation sector is experiencing the impacts of climate change on both in-flight and ground operations. One meaningful example is that of heatwaves, as temperature extremes reduce aircraft lift and takeoff performance, requiring lighter loads—including fewer passengers and less cargo— and increasing noise pollution due to shallower climb angles. Heatwaves also deteriorate runway asphalt and compromise comfort inside airport terminals.
In this work, we analyze the occurrence of summer heatwaves at 30 European airports by comparing historical data from 1985-2014 with projections for 2035-2065, using data from 10 CMIP6 models across three Shared Socioeconomic Pathways (SSP126, SSP370, SSP585). We apply a dedicated bias correction and downscaling technique to model data to improve spatial resolution and address systematic distributional biases. Heatwaves are identified and measured using a Standardized Heatwave Indicator (SHI), enabling the assessment of any trends in the frequency, length, and intensity of this phenomenon at each airport.
Our findings reveal a significant increase in the frequency, duration, and intensity of summer heatwaves across all the airports considered for the period 2035-2065 compared to historical data. Heatwaves are anticipated to become an annual phenomenon, with Southern European airports expected to face the most severe conditions. These airports could in fact experience an average of up to four heatwaves each summer, lasting up to 14 days and reaching intensities exceeding the 99.97th percentile of historical events.
This work underscores the urgent need to identify and implement heatwave-aware adaptation measures and operational improvements in aviation, enhancing the climate resilience of this sector to ensure safety and efficiency in operations.

How to cite: Guerrini, F., Trentini, L., Venturini, M., Dal Gesso, S., Calmanti, S., and Petitta, M.: Climate Change and Aviation: The Increasing Impact of Heatwaves on European Airports, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-939, https://doi.org/10.5194/ems2024-939, 2024.

In recent years, interest in the study of natural disasters has grown considerably, linked both to the increase in the number of such events and to the worsening of their impact on the territory. The conservation strategies of cultural heritage (whether it is movable or immovable), in response to damage resulting from the natural phenomena of aging and decay but also from the occurrence of disasters, inevitably require a methodological approach aimed at planned conservation and preparedness for the risk event of the cultural site. In this sense, the Mediterranean basin which is a unique environment for the occurrence of atmospheric phenomena, results to be a relevant example, considering that Italy currently has the largest number of sites inscribed on the World Heritage List. We examined with this aim some case studies in Italy. We have employed different meteorological data (Satellite-based observations, ERA5 model, NWP models, GNSS, Lightning detection network, Radar) to characterize events related to some case studies of potential interest. The meteorological events investigated, which caused flooding and damage in the Italian study areas, showed how the sites responded in different ways to the stresses. The architectural peculiarities, the reciprocal relationship between buildings and environment, typical of spontaneous architecture in the Mediterranean basin, and the perception of heritage by inhabitants strongly condition protection policies. In this context, the properties of buildings need to respond to three fundamental aspects: climate, building materials and the morphology of the territory. These distinctive properties have therefore contributed to sites resilience, as explored in more detail in the different works performed (UNESCO archeological site of Villa Romana del Casale, in Piazza Armerina (Sicily), UNESCO site of “Sassi di Matera”, Alberobello UNESCO site, Como UNESCO Creative City and Trabocchi Coast (Abruzzo) UNESCO intangible cultural heritage candidates). Results have shown the great potential of the multi-sensors approach to describe in the most complete and detailed way the intense meteorological events and to show the agreement between the products involved in the studies in order to evaluate the joint use of different platforms. Future multi-instrumental applications and potential use of some products (e.g., satellite-based) for the analysis of events of interest in areas not covered by operative tools is an issue that emerged. In particular, in all presented case studies, we started from a long-term and large-scale analysis concerning the climatology of the area, to a short-term and more local scale where we described the most acute phases of the events. Different responses to stresses have to indicate the most proper approach to face up the risks and plan the actions to preserve the cultural heritage (e.g. risk management plan, mandatory for each UNESCO site since 2019).

How to cite: Mascitelli, A., Prestileo, F., Federico, S., Torcasio, R. C., Di Carlo, P., and Dietrich, S.: Multi-sensor approach for analysis of extreme weather events affecting areas of cultural interest, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-941, https://doi.org/10.5194/ems2024-941, 2024.

How to cite: Aparicio Garcia, A., Rius, A., Barbería, L., Miró, A., Peña, J. C., and Ruiz, A.: Enhancing meteorological report access and response time: How the Meteorological Service of Catalonia addresses the rise in weather-related claims, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-234, https://doi.org/10.5194/ems2024-234, 2024.

How to cite: de la Vara, A., Cabos, W., and Matamoros, A.: ECOAZUL-MED climate service tool: supporting aquaculture, fisheries and coastal tourism in the Spanish Mediterranean coasts , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-266, https://doi.org/10.5194/ems2024-266, 2024.

This study investigates climate challenges at national level focusing mainly in two prominent coastal destinations, Costa Daurada and Terres de l'Ebre, situated in Catalonia (northeastern Spain), within the southeastern Mediterranean region. With the urgency to bolster societal resilience to climate hazards and manage risks in these Mediterranean locales, adapting tourist destinations to climate change and mitigating associated risks demand a comprehensive understanding of behavioral change factors affecting businesses, citizens, tourists, and administration.

Central to the climate adaptation strategy for the tourism sector is access to high-quality Climate Services tailored to end-users' needs. These services function as decision-making tools, offer incentives for social benefits, and communicate effectively, as highlighted by Scott et al. (2011). The Horizon2020 Impetus project, commenced in October 2021, aligns with the EU's 2050 climate goals, translating commitments into actionable steps for community and planetary protection.

Focusing on achieving climate-resilient tourism and implementing the Catalan Climate Change Adaptation Strategy (ESCACC30) in the tourism sector, the project seeks a fundamental behavioral shift. Recognizing the pivotal role of Behavioral Change, the strategy adopts a problem-solving approach based on Michie et al.'s (2013) taxonomy, meticulously examining factors impacting behavioral change, devising strategies to overcome challenges, and enhancing change facilitators, while also incorporating measures to prevent setbacks and address climate change consequences.

Inspired by Font et al.'s (2021) methodology, the co-creation process involved public and private stakeholders from Costa Daurada and Terres de l'Ebre actively participating in workshops. These workshops focused on themes such as weather, climate change, tourism, energy, water, and the perception of the beach-dune system. Through this collaborative effort, stakeholders developed indicators shedding light on the climatic potential for various tourism modalities, identifying physical impacts of climate change affecting the attractiveness and vulnerability of the tourist destinations.

Proposed measures related to water and energy were classified based on priority and vulnerability. Additionally, the repeat photography method was employed to gather data on citizens' awareness of temporal changes in the beach-dune system within the study area. This approach also provided insights into their perceptions regarding the roles and landscape of the beaches and dunes, ultimately contributing to the development of climate co-created indicators essential for effective climate adaptation in these Mediterranean coastal destinations.

How to cite: Boqué-Ciurana, A., Saladié, Ò., Garcia-Lozano, C., Borràs, G., Martí, C., Rovira Soto, M. T., Aguilar, E., Plana Puig, Q., and Cester, I.: Driving Change: User Engagement and Governance in Climate Adaptation for Coastal Destinations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-523, https://doi.org/10.5194/ems2024-523, 2024.

Climate change impacts every aspect of human and ecological systems, yet its effects on cultural outdoor activities remain underexplored, an oversight this project aims to start correcting. Specifically, this research focuses on the Catalan tradition of "Castellers," which involves constructing human towers, a deeply rooted cultural practice in some Catalan regions symbolising community strength and cooperation. Recognizing the potential vulnerability of this tradition to changing climate conditions, this study develops a tailored analytical framework to assess these impacts, building upon the co-creation of climate service methodology by local agents outlined in Font et al. (2021).

To capture the specific impacts of climate change on the "castellers," we conducted workshops with different "colles" (the groups that build the towers) to adapt the existing methodology better to fit the unique needs of this cultural practice. These collaborations have led to the creation of a specific indicator designed to measure the sensitivity of the "castellers" to environmental changes.

A key component of our research methodology involved measuring Essential Climate Variables (ECVs) during tower-building exhibitions, among the general description based on station and reanalysis data. Instruments were set up in the “places” where these events typically take place to collect real-time environmental data, such as temperature, humidity, and wind speed, during the performances. This approach allowed for the accurate capturing of the climatic conditions faced by participants during their performances, providing a robust dataset for analysing the potential impacts of climate variability on this activity.

The initial findings from this data collection indicate that even slight changes in ECVs can influence the performance and safety conditions of the "castellers." With these insights, the project seeks to equip organizers, participants, local government bodies in their capacity as risk managers, and the audience with valuable information to make informed decisions that help mitigate risks and ensure the sustainability of this intangible cultural heritage. This study highlights the vulnerability of cultural practices to climate change and sets a precedent for similar research into other areas of intangible heritage.

How to cite: Olano Pozo, J. X., Boqué-Ciurana, A., and Saladié Borraz, Ò.: Actionable Climate Information to take decisions: Firts insights of the co-created climate indices for cultural outdoor activities in Catalonia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-692, https://doi.org/10.5194/ems2024-692, 2024.

How to cite: Fischer, C., Vogt, T., Beckert, A., Fuchs, S., Radke, T., and Rautenhaus, M.: Novel 3-D and AI-based weather forecast products based on open data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-949, https://doi.org/10.5194/ems2024-949, 2024.

It’s common to hear comments about the perceived gap between science (and scientists) and society (and citizens), or about the distinct language used by scientists and by citizens. Trying to avoid these comments, particularly concerning climate change, the Meteorological Service of Catalonia (SMC) publishes every year the Annual Bulletin of Climate Indicators (BAIC, from its name in Catalan) in Catalonia, with updated information on the observed evolution of various climate variables and climate change indices since 1950.

The primary goal of this annual bulletin is to provide in-depth analysis of the evolution of recent climate in Catalonia and to highlight evidence and impacts of climate change across different sectors such as forests, agriculture, or maritime activities, among others. Given the significant acceleration of global warming due to human activities in recent decades, there is pressing need for an objective assessment of this phenomenon to carry out effective mitigation and adaptation measures at regional and local scales, and BAIC meets this need.

This bulletin is structured into several chapters, most of which focus on monitored climate variables or systems: an introduction, air temperature (including maximum, minimum and mean temperatures for 27 climate series), precipitation (data for 72 climate series), extreme climate indices (following ETCCDI definitions), secular observatories (providing information from specific observatories in Catalonia with more than 100 years of daily data), maritime variables (such as sea temperature at different depths, mean sea level, sea storms) and phenology (information from the Phenological Network of Catalonia). Additionally, there are plans to increase the number of chapters in the future, potentially covering topics such as changes in synoptic circulation over Catalonia (or northwestern Mediterranean area). After completion of the bulletin, an Executive Summary is always written to summarise the key points of the main bulletin. Both reports, BAIC and its executive summary, can be consulted (and downloaded) on the SMC website (https://www.meteo.cat/wpweb/climatologia/butlletins-i-episodis-meteorologics/butlleti-anual-dindicadors-climatics/).

In this work, we will show the main results of the latest bulletin and provide examples of various types of figures (such as graphics and charts) used to convey these results to the end-users. We always strive to show the information in the clearest and most accessible manner possible, while also maintaining scientific rigor (ensuring clarity, conciseness, and robustness). The key findings of the bulletin include:

• An increase of 1.9 ºC in annual mean temperature since 1950.
• A statistically significant reduction of 17 % in annual precipitation since 1950.
• All extreme climate indices related to high temperature values show a significant increase, while those associated with low temperature values exhibit a notable decrease.
• Finally, the territory is currently experiencing the most severe drought since 1916 (instrumental period).

How to cite: Cunillera, J., Prohom, M., Busto, M., Herrero, M., Barrera-Escoda, A., Barnolas, M., and Altava, V.: Informing about climate change evidence to society: the Annual Bulletin of Climate Indicators in Catalonia (Meteorological Service of Catalonia), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-330, https://doi.org/10.5194/ems2024-330, 2024.

On the afternoon of 30th August 2022, a powerful supercell thunderstorm in northeastern Catalonia, Spain, produced a giant hail episode. This rare event resulted in one fatality, over 70 injuries and substantial property damage. According to the records of the severe weather of the Meteorological Service of Catalonia (SMC), this is the largest hailstone ever recorded by Meteorological Network Spotted (XOM). 

The focus of the study is to understand why hailstones of exceptional size, reaching 10 cm in diameter, were produced by this storm. For this purpose, firstly we consulted the observations recorded by the local people to identify the path of the hailstorm and the different affectations along the way. Next, we combined the remote sensing data to reveal the severe weather signatures. The radar fields revealed large reflectivity strong vertical development, and very large forward anvil, which measured over 40 km in length. Complementary to this, the total lightning flash rate steeply increased at the same time as an intensification of severe weather radar signatures (tilting, three-body scatter spike, BWER-Bounded weak echo region). The satellite imagery allowed the cold ring pattern detection and extreme cold overshooting top-down to -64 ºC. 

On the other hand, the synoptic, mesoscale, radiosonde, and cross-sections run by WRF 3 km and 1.5 km were evaluated to characterize the thermodynamic and environmental conditions favourable for the growth of a supercell with large-hail potential. The values of CAPE in the coastal line were > 2.500 J/kg, the Total Precipitable Water was > 4 cm, and the hodograph signature revealed a strong low-level inflow and strong upper outflow. 

How to cite: soler Temprano, X., Rigo Ribas, T., Farnell Barqué, C., Batalla, E., Pineda Rüegg, N., Mercader, J., and Martín Vide, J.: Reanalysis of Giant Hail Event in Catalonia (NE of the Iberian Peninsula), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-474, https://doi.org/10.5194/ems2024-474, 2024.

The 3D Lightning Mapping Array (LMA) is a regional network of VHF antenna stations spaced 5-50 km apart that detect lightning pulses in the 60-66 MHz band. The pulses are timed by GPS. Time-of-arrival reconstruction results in 3D pulse locations, revealing the lightning leader channels (of negative polarity) inside the cloud and toward ground. The detection range of a sensitive network can reach over 250 km from the center. LMAs are used for lightning science, nowcasting of severe weather and airspace safety.

In 2011, the UPC Ebro 3D Lightning Mapping Array in Spain was the first LMA to be installed outside the USA and has collected data for about 11 years. This system was replaced in 2023 by 13 (up to 15) stations spread out mostly across western Catalonia, operated on solar power. Additionally, the Meteorological Service of Catalonia started operating their LMA network in 2024. Each network can operate independently, but data from both networks can be combined to offer the best spatial coverage and resolution of real-time lightning monitoring.

As the duration of processing time-of-arrival into lightning location data goes up exponentially with the number of stations in the network, smart approaches of combining small groups of stations in parallel and pooling the solutions are investigated.

This poster will focus on the capabilities of newly designed interactive lightning visualization and analysis tools for the LMA, developed using the Julia programming language.

It is a suite of tools that focus on (a) lightning flash analysis and general browsing of activity, (b) storm activity evolution analysis, and (c) network performance analysis based on the data. Lightning flash analysis focused on the structure of the lightning flash. Flash grouping is based on DBSCAN clustering, and sparkle activity near the cloud top can be isolated similarly. An automated leader speed using the Theil-Sen slope estimator of points runs efficiently on a large dataset, and can be used to highlight regions of positive and negative polarity in the cloud. The tool also can read cloud-to-ground stroke data from other networks, and Meteosat Third Generation Lightning Imager or GOES Geostationary Lightning Mapper (GLM) to be compared with LMA. The storm activity analysis tool will be based on local 3D flash density and its evolution in time and aims to study trends in 3D activity, rather than the ubiquitously used general flash rate. Lightning leader speed, polarity and the repetition rate of leaders in flashes may hold new information that could turn out to be useful in monitoring the thunderstorm state and potential for severe weather.

How to cite: van der Velde, O., Romero, D., López, J., Montanyà, J., Pineda, N., and Fabró, F.: New visualization and analysis tools for 3D Lightning Mapping Array data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-978, https://doi.org/10.5194/ems2024-978, 2024.

Drought episodes are among the most devasting natural disasters impacting agriculture, forests, the environment, and economic activity. Catalonia, in the Northwestern Mediterranean basin, is currently experiencing its most severe drought episode on record.
For a comprehensive examination of drought episodes, it is crucial to consider different processes involved in water balance. However, many studies tend to focus solely on precipitation scarcity, often overlooking other critical variables like evapotranspiration or approximating it through simplified methods.
While uncertainty persists regarding present and future precipitation patterns in the Mediterranean basin, the temperature is showing a steady increase and is projected to rise even more in the future. This rise could intensify evapotranspiration rates, exacerbating drought episodes. 
Nonetheless, it is essential to note that evapotranspiration is not only influenced by temperature; other meteorological variables such as solar radiation, relative humidity, and wind also play significant roles. 
This study aims to analyze the relative impact of different meteorological variables on evapotranspiration to better understand the factors affecting this relevant phenomenon for drought management in Catalonia. Such insights will provide valuable information for adapting to future droughts episodes.
The study proposes an  in-depth analysis of evapotranspiration in Catalonia, using data collected by the XEMA observation network of the Meteorological Service of Catalonia (SMC). This network extensively covers the region, providing data for calculating evapotranspiration on  monthly, daily, and even hourly time scales. Taking advantage of this dataset, the Penman-Monteith formulation is employed to compute reference crop evapotranspiration (ETo). The evolution of this variable in the territory, its spatial distribution, and case studies such as the recent 2020-2024 episode are studied.

How to cite: Cortès, M. and Altava-Ortiz, V.: Understanding reference evapotranspiration distribution and evolution in the Northwestern Mediterranean basin, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-327, https://doi.org/10.5194/ems2024-327, 2024.

With the development of machine learning (ML), it provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, JMA CPS2 model data, we conducted the multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms,, the decision tree (DT), the random forest (RF), and the adaptive boosting (AB) algorithm. On this basis, we explored the applicability of ML algorithms to ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy.  Then, the MME predictions based on optimal hyperparameters were constructed for different regions of China. The results show that all three ML algorithms have an optimal maximum depth less than 2, which means that based on the current amount of data, the three algorithms can only predict positive or negative precipitation anomalies, and extreme precipitation is hard to predict. The importance of each model in the ML-based MME is quantitatively evaluated. The result shows that NCEP CFSv2 and JMA CPS2 have a higher importance in MME for eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provides a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME has a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019-2021, which exhibits a significant improvement over other methods. It shows that ML methods have great potential in improving short-term climate prediction. These results provide an important reference for short-term climate prediction in China. ML-based MME has the potential to accurately forecast the main rainbands of summer precipitation in China.

How to cite: Yang, J.: Multi-model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-304, https://doi.org/10.5194/ems2024-304, 2024.

Abstract: With the development of technology, major operational centers and scientific research institutions have carried out research and development of convection permitting ensemble prediction technology and systems. In order to meet the needs of accurate forecasting in the capital, the Convection permitting ensemble prediction system of North China CMA-BJ-EN v1.0 developed by the Institute of Urban Meteorology was officially put into operation in January 2023. This paper introduces the research and development background and key technologies of the system in detail. The results show that the CMA-BJ-EN v1.0 system has a resolution of 3km convective resolution scale, covering 21 members, and can provide hourly probability prediction results of 48h forecast effectiveness in North China; The system is coupled with advanced technologies such as initial conditon perturbation of ensemble data assimilation, stochastic physical process tendency(SPPT) model perturbation, and has complete pre-processing, initial condition perturbation, model perturbation, and post-processing configuration, which can provide a series of probability prediction products; The qualitative and quantitative evaluation of CMA-BJ-EN v1.0 system and its comparison with NCEP global ensemble forecast system show that the system can effectively grasp several major weather processes in North China, and can obtain local refined probability forecast results relative to global ensemble; The statistical results also show that compared with the NCEP GEFS global ensemble forecast, the CMA-BJ-EN v1.0 forecast system can effectively reduce the RMSE of 2m temperature and 10m wind speed, and can effectively improve the probability forecast score of precipitation forecast. The operation of the system can provide users with effective refined probability forecast reference.

Keywords: convection permitting, ensemble prediction system, initial condition perturbation, model perturbation

How to cite: Zhang, H. and Xia, Y.: Introduction of operational convection permitting ensemble prediction system of North China, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-307, https://doi.org/10.5194/ems2024-307, 2024.

Climatologically, typhoons are generated over the western North Pacific (WNP) and move towards the East Asian countries, such as China, Korea, and Japan, etc. The typical track of typhoons in this case is a C-type curve, and they change their track over the ocean in the midlatitude. This turning point of the track of typhoon is called the recurvature point. However, the untypical track typhoon frequently occurred in recent years and the track forecast is difficult.
For example, the 6th typhoon Kanun in 2023, made two sharp recurvature while moved to the north. Kanun has an increase in track error in the early and mid-stage of prediction, which is estimated to be an error caused by the inability to accurately predict the two recurvature point. The maximum error of the models was approximately 400km for the 72-hour forecast and approximately 700km for the 120-hour forecast, respectively. This is larger than the average error of other typhoons in 2023 as well as the overall error average of Kanun. The question of why is the Kanun’s track error is larger than average is raised.
This study is motivated in order to explain the reason of unusual large track error of typhoons. In the first step, we investigate the two unusual track typhoons errors such as, 11th typhoon Hinnamnor in 2022 and 6th typhoon Khanun in 2023. The model track error will be analyzed by calculating the error at the recurvature point of 102 members of the global ensemble model of KIM (Korean Integrated Model, 26 members), UM (Unified Model, 25 members), and ECMWF (The European Centre for Medium-Range Weather Forecasts, 51 members), respectively. 
The individual prediction data of the ensemble members rather than the ensemble mean is used. The recurvature point is automatically calculated from the predicted track at each issue time and compared with the recurvature point calculated on the best track. The prediction trend and performance of the ensemble model are identified by analyzing the distribution of the forecast time error and forecast position error at the recurvature point. Furthermore, the prediction field is classified and diagnosed according to the forecast error.

How to cite: Yun, S., Shin, H.-C., Cha, E.-J., Kim, E.-J., Park, J. I., Choi, W. J., and Ha, J.-C.: Diagnostic study of the forecast accuracy of the ensemble models at the recurvature point of the typhoon, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-390, https://doi.org/10.5194/ems2024-390, 2024.

The land-atmosphere coupling approach in current state-of-the-art NWP systems is based on weakly coupled data assimilation systems for individual Earth-system components. Atmospheric and land surface analyses are performed separately, and results are fed back into the next data assimilation window based on a model forecast. This can lead to imbalanced initial conditions or shocks and the observations cannot be fully harnessed for all components when assimilated only into one Earth system component within the same window.

The CopERnIcus climate change Service Evolution (CERISE) project aims to advance coupled surface-atmosphere assimilation in the preparation of the next generations global and regional reanalysis systems. As for the land, ECMWF’s activities are towards a unified Land Data Assimilation System (LDAS) based on the Simplified Extended Kalman Filter (SEKF) that incorporates multi-layer soil moisture analysis in operations and is currently being extended to other variables, making it suitable for improved coupling.

As part of CERISE, a “quasi-strongly coupled data assimilation” is being developed based on "outer land-atmosphere coupling" approach. Aim is to activate the SEKF as part of several 4D-Var outer loops and return the updated land analyses to initialize the atmosphere and the land of next outer loops within the same assimilation window. Initial efforts have focused on infrastructure developments to enable running the SEKF within the 4D-Var non-linear trajectory.

This work presents the preliminary results of scientific activities, numerical experimentation, and preliminary results to identify the proper number of outer loops required for optimal coupled land-atmosphere assimilation by testing different coupling configurations. For variables that are subject to strong diurnal cycles – such as soil and skin temperature - balanced initial conditions between the different outer loops can be advantageous. The new infrastructure has the capability to improve the exploitation of interface observations (e.g. land surface temperature) so that they can simultaneously influence the analysis of multiple Earth system components.

How to cite: Herbert, C., Weston, P., de Rosnay, P., Fairbairn, D., and Pinnington, E.: Exploring outer-loop land-atmosphere coupling, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-530, https://doi.org/10.5194/ems2024-530, 2024.

The precipitation changes over mainland Southeast Asia (SEA) are important to water resources in many countries in this region. The diurnal cycle of precipitation is a crucial aspect of the local climate. The sub-daily convective activities and precipitation variability are strongly modulated by the surrounding complex topography However, the intricate interplay between topographical features and local atmospheric processes, and how they shape the spatial distribution of diurnal precipitation variability across SEA, remains unclear. This study investigates diurnal precipitation patterns and associated physical processes, particularly focusing on modeling the diurnal cycles in this region using convection-permitting models (CPMs). To investigate the effect of model resolution on diurnal precipitation and associated processes, we conducted two high-resolution simulations using the Weather Research and Forecasting (WRF) model driven by ERA5 at spatial resolutions of 9 and 3 km, focusing on summertime (June-August) during 2002-2005. We compared the output from the two WRF experiments to ERA5 and observation-based datasets, including in situ observations (GHCN-D), and gridded observations (APHRODITE, IMERG). The diurnal patterns in space were clustered into 5 distinct groups based on K-means classification. Compared with the ERA5 reanalysis, the two high-resolution WRF simulations show a reduced wet bias relative to IMERGE and better captured intense precipitation events found in the in situ measurements, while the precipitation in ERA5 is more similar to APHRODITE. Furthermore, the results show that the WRF simulations outperform ERA5 in capturing the spatial patterns of precipitation intensities and peak time, especially in mountainous and coastline regions, using IMERG as the reference. These differences can be explained by differences in convective available potential energy (CAPE) between the WRF simulations and ERA5, as well as near-surface winds. Between the two WRF simulations, the 3-km WRF simulation displays weaker precipitation intensities compared to the 9-km WRF simulations, which better match the hourly IMERG data, while the 9-km WRF simulations perform better in peak time of diurnal precipitation. Assessing the benefits of higher-resolution modeling is challenging because the benefits vary between variables and regions. In conclusion, the work highlights the importance of applying CPMs to capture diurnal cycles of precipitation, local convective activities (CAPE), and near-surface winds over complex topography.

How to cite: Lai, H.-W. and Ou, T.: Investigating the Diurnal Cycle of Summer Precipitation over Mainland Southeast Asia: Insights from Dynamic downscaling Simulations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-25, https://doi.org/10.5194/ems2024-25, 2024.

The coastal regions of Antarctica are subject to frequent passages of low-pressure systems and transient pressure ridges. This synoptic-scale setup favors abrupt weather changes and occurrence of dangerous outdoor conditions such as snowstorms, extremely low wind-chill values due to low air temperature and strong winds, or intense solar radiation potentially leading to snow blindness. In this coastal zone most Antarctic research stations are located, and numerous scientific and logistical field activities are conducted. Under these circumstances, operational weather forecasts are of utmost importance. In this contribution, a WRF-based high-resolution weather prediction system is presented and evaluated. This experimental system was run to support the 2023 and 2024 summer expeditions to the J. G. Mendel Station on James Ross Island, Antarctic Peninsula. Initial and boundary conditions were provided by the GFS model, model topography by the Reference Elevation Model for Antarctica. The model configuration included the 3DTKE boundary layer scheme suitable for sub-kilometer resolutions, Thompson microphysics, RRTMG longwave and shortwave radiation schemes and the NoahMP land surface model. The model was run once a day in 500-m horizontal resolution (132-h lead time) and 1.5-km horizontal resolution (96-h lead time, more recent initial conditions). Forecasted time series of air temperature, wind speed and direction, precipitation amount, snow height, global radiation and sea-level pressure for multiple field locations were sent to the Mendel Station via a satellite internet service. The WRF model forecasts were validated with in-situ observations at the coastal Mendel Station (10 m a.s.l.) and the top of Davies Dome glacier (514 m a.s.l.). Furthermore, the model accuracy was compared with the output of publicly available Antarctic Mesoscale Prediction System (AMPS). Compared to AMPS, the WRF model in 500-m resolution massively improved air temperature prediction at Mendel Station, reducing mean bias from -4.2 °C to -0.8 °C in 2023. In late 2023, multiple AMPS physical parameterizations were updated, possibly contributing to reduced bias of -2.9 °C in the 2024 season. However, the WRF model still performed significantly better with bias of ‑0.5 °C. On Davies Dome, the WRF model performed slightly better in 2023 (by 0.4°C) while in 2024 the models performed similarly well.  Regarding wind speed, both the WRF model and AMPS provided comparable results with mean bias 1.5 - 1.9 m·s^‑1 at Mendel Station (more favourable for WRF) and 0.1 - 0.7 m·s^‑1 on Davies Dome (more favourable for AMPS). Prediction of two significant snowfall events in 2023 was done with a very good accuracy.

How to cite: Matějka, M. and Láska, K.: Operational weather forecasting in Antarctica with the WRF model in sub-kilometer resolution, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-566, https://doi.org/10.5194/ems2024-566, 2024.

The All-Sky Radiance (ASR) data from geostationary satellites are important for improving initial conditions in numerical modeling through data assimilation, as it provides dense spatio-temporal atmospheric information over a wide area. Accurately applying the error information inherent in observations is essential for enhancing its effectiveness of satellite data assimilation. In this study, we calculated an observation error model for the ten infrared radiation channels of the Advanced Meteorological Imager (AMI) on the GEO-KOMPSAT-2A (GK-2A) for the summer season using the standard deviation of the brightness temperature observation minus background (O-B) as a function of the cloud impact parameter (Ca). The normalized brightness temperature of O-B probability density function is scaled such that it more closely approximates a normal distribution. For data assimilation experiments, we used the Community Radiative Transfer Model (CRTM) as the satellite observation operator and applied the 3-dimensional variational data assimilation method of the Weather Research and Forecasting Model Data Assimilation. When applying the adjusted observation error model for summer precipitation cases in the Korean peninsula, both the analysis and forecast fields improved compared to a prescribed constant error value. The best rainfall forecast performance was observed in the linear model, which followed the normal distribution better than the high-order regression observation error model. This is thought to be due to the observation error in the linear model saturates more gradually, allowing for consideration of a wider variability of Ca, i.e., a more detailed spatial distribution of cloud impact. Meanwhile, the assimilation results of Clear-Sky Radiance (CSR), excluding cloud area information, were compared to analyze the additional effects of cloud-precipitation area information during ASR assimilation. Further, we plan to assimilate both the water vapor channel ASR and the surface-sensitive channel CSR to improve the cloud detection algorithm, quality control, and refine surface parameter estimates for enhanced predictability.

Key words: GK-2A infrared channels, data assimilation, observation error, precipitation forecast

※ This research was supported by the National Research Foundation of Korea (No. 2022R1A2C1012361) funded by the Ministry of Science and Technology, and also was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740. Additional support was provided by the BK21 FOUR project funded by the Ministry of Education.

How to cite: Min, K.-H. and Jo, S.-Y.: Analysis of Predictability Improvement due to the Enhancement of Observation Error for the GK-2A Infrared Channels in Data Assimilation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-981, https://doi.org/10.5194/ems2024-981, 2024.

 The Korea Peninsula is surrounded by the sea on three sides and is made up of mountainous areas more than 70% of the land, making it difficult to predict and verify precipitation using numerical models. To overcome these topographical features, a dense observation network and 10 radar site have been operated in Korea Meteorological Administration (KMA). Also, a regional prediction system based on the Korea Intergrated Model (RDAPS-KIM) has been operated since May 2022. RDAPS-KIM covers the East Asia region with a finer horizontal resolution(3km) than the global model KIM(12km) and simulates real complex terrain closely.
 Traditional skill score to verify models assesses the rainfall prediction performance for the grid closest to single point locations, which has the double penalty problem that the forecast precipitation exhibits the same pattern as the observation but leads to worse prediction evaluations due to missing spatial displacement. This double penalty issue occurs more frequently in regional model when the rainbands were shifted, so it can lead to misconception that regional model underperform global model. In order to avoid this problem, spatial verification methods to evaluate probability of rainfall forecast in the surrounding area have been suggested in the previous study (Ebert, 2008). The Fraction Skill Score (FSS) is one of the spatial verification methods suggested Roberts and Lean (2008), which allows the comparison of predicted precipitation with a spatial truth fields such as radar data. Accordingly, by verifying prediction with probability, it is expected that the shortcomings of verification of high resolution models can be overcome. Also, the FSS is recommended for verification in numerical weather prediction models by World Meteorological Organization (WMO) (JWGFVR, 2013).
 In this study, the models of KMA (KIM and RDAPS-KIM) are assessed using FSS method compared with different sized neighborhoods and various rainfall threshold for 3h-accumulated precipitation. Analysis indicated how the spatial scale influences the FSS values, showing that FSS increases as neighborhood size increases. The use of selected one proper neighborhood size pointed out that RDAPS-KIM lead to a high FSS than KIM, because it fits the rainbands better. The result shows that RDAPS-KIM is more efficient for heavy precipitation and local rainfall than KIM. In the future, we will operate the higher resolution 1km model to improve prediction performance in simulating severe weather events, and evaluate in combination with traditional techniques and probabilistic forecasts. It is expected that interpretation of accuracy of precipitation forecasts from various perspectives will be possible.

How to cite: Jeong, S., Lee, J., Lee, E., and Kim, S.: Analysis of the Fraction Skill Score for the rainfall verification of high resolution model in KMA, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-618, https://doi.org/10.5194/ems2024-618, 2024.

Recent years have witnessed significant advancements in the development of Artificial Intelligence (AI) tools, notably Large Language Models (LLMs), with prominent systems including ChatGPT by OpenAI, Gemini by Google, and Copilot by Microsoft. Despite inherent limitations, the diversity of these tools' applications across various fields of life, including scientific research, has expanded significantly.

This study evaluates the utility of various AI tools within the fields of meteorology and climatology, ensuring their applications follow ethical standards in scientific publication. The tools assessed include ChatGPT versions 3.5 and 4.0, Gemini (Google), Copilot (Microsoft), Perplexity, and GPT-based systems such as DataAnalyst, Consensus, ScholarGPT, and Academic Assistant Pro, among others. Each tool was subjected to identical inputs (prompts, data, photographs) and their responses were evaluated on a 0-10 scale for accuracy and relevance. The scoring was based on the percentage of verifiable content in the responses to ensure objectivity. The research spanned from May 2023 to April 2024.

The AI systems were tasked with responding to queries on climate change in Poland, identifying key research papers on humid heat waves, classifying cloud types, creating a climate map from provided data, and comparing two climate maps.

The outcomes varied significantly across tasks. ChatGPT 3.5 demonstrated an answer accuracy of 30-40% (topic: climate change in Poland). The Consensus system excelled in identifying and summarizing key papers on humid heat waves research. ChatGPT 4.0 emerged as the most effective tool for cloud classification, with Copilot also delivering commendable results; however, Gemini (Advanced) struggled with cloud recognition tasks. DataAnalyst proved capable of generating basic climate maps, but with some inaccuracies such as station misplacements. When comparing two climate maps, all systems performed adequately, with the most precise descriptions provided by Bard (Google).

How to cite: Krzyżewska, A.: The application of AI tools in weather and climate science, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-180, https://doi.org/10.5194/ems2024-180, 2024.

How to cite: Casellas Masana, E., Miró Cubells, J. R., and Moré Pratdesaba, J.: Postprocessing multi-model ensemble temperature forecasts using Distributional Regression Networks, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-322, https://doi.org/10.5194/ems2024-322, 2024.

The ability to obtain high spatial resolution meteorological data from coarse sources is a crucial skill needed to study local phenomena happening at finer scales such as severe storms or convective systems. This spatial downscaling can be achieved by reproducing the atmospheric state of a small region using numerical weather prediction (NWP) models that use low-resolution (LR) data as boundary conditions. However, running NWP models at high resolutions is computationally expensive and time consuming. A different approach is to establish statistical relationships between LR and HR data to increase the spatial resolution by interpolating intermediate points. In recent times machine learning (ML) based statistical methods have proven to be a cheap yet accurate alternative to dynamical downscaling. 

How to cite: Benitez, M., Margalef, T., Rodríguez, M., and Dutta, O.: Comparing Deep Learning methodologies for Downscaling between meteorological models, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-491, https://doi.org/10.5194/ems2024-491, 2024.

The generation of accurate and reliable forecasts of near-surface (~10 m above ground level) gridded wind speed data, hereinafter called NSWS, is crucial since it influences numerous socioeconomic and environmental fields. For instance, in the face of climate change, wind energy can contribute to the decarbonization of the electricity grid. NSWS, however, is a complex meteorological variable due to its inherent space-time variability, particularly in regions with complex topography like Valencia (Spain).

The traditional approach to forecasting NSWS relies on Numerical Weather Prediction (NWP) models, which demand substantial computational resources, specially when high spatial and temporal resolution are required, often necessitating hundred to thousands of CPU hours. As an innovative solution to this pressing issue, the ThinkInAzul project, under Climatoc-Lab, is exploring the use of deep learning for accurate NSWS predictions. We propose an architecturebased on encoder-decoder neural networks composing mixed convolutional and recurrent  (ConvLSTM) layers. This AI-based product, designed as an early warning system, generate high-resolution (3- or 9-km) short-term (i.e., <24 hours) NSWS forecasts in near real-time (a few seconds) using a GPU.

Meteorological station networks provide realistic observations, being able to detect local wind effects, but with limited spatial coverage. Conversely, reanalysis and simulation products offer complete spatial coverage at low resolution but fail to accurately reproduce local NSWS. To address this, our AI-based tool is trained with the ERA5-Land (9-km) and NEWA (New European Wind Atlas, 3-km) NSWS datasets but its inference is performed using the observations from the Spain/Valencian Association of Meteorology (AEMET/AVAMET), a citizen weather station network of around ~600 stations. Consequently, the AI-based tool merges the advantages of both, offering a gridded product with high spatio-temporal resolution that can reproduce local NSWS effects.

The AI-based tool achieves a reasonably high correlation of 0.7 with the AEMET meteorological observations, with expectation of further improvement. This tool is applied to the western Mediterranean coast and has the potential for use in other regions following retraining of the neural network. Our ultimate goal is to develop an AI-based tool that enhance short-term forecasting of NSWS.

How to cite: Martinez-Roig, M., P. Plaza, N., Azorin-Molina, C., Andres-Martin, M., Chen, D., Zeng, Z., Vicente Serrano, S. M., R. McVicar, T., Guijarro, J. A., and Ali Safaei Pirooz, A.: AI-based approach for short-term forecasting of wind speed from a weather station network: A Case study in Valencia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-536, https://doi.org/10.5194/ems2024-536, 2024.

Air pollution is currently a major environmental issue to human health and natural ecosystems so improving air quality monitoring techniques, traditionally based on ground-based observation networks, is essential. Satellite remote sensing of air pollutants has made significant strides in recent years and now serves as a complementary data source alongside ground sensors. For example, different studies have explored the relationship between satellite-derived NO₂ total column data and ground-level concentration but none of them focused on complex terrain areas. The aim of this work is to evaluate the feasibility of using NO₂ column data from the Sentinel 5P satellite over complex terrain such as the Pyrenees Mountain area covering France, Spain and Andorra to estimate ground level values. For this purpose, a number of models considering the separation of temporal average and fluctuations are considered for both satellite and ground sensor data. The primary objective of these models is to enhance the signal-to-noise ratio. Initially, the periodicities are identified and subtracted from the original data, resulting in a residual series. These residual series are then filtered to eliminate noise while retaining the significant events. Finally, these new series are combined with the previously identified periodicity.  

Preliminary results over Andorra show that our models can enhance Pearson's correlation between the temporal series of the satellite and ground sensor, improving it from 0.415 to 0.650. In addition, it has been found that the NO₂ annual cycle in Andorra can be detected with a correlation of 0.950 between the model and the ground sensor NO₂ series. Furthermore, a weekly cycle during winter has been detected in the Sentinel NO₂ series too. These findings suggest that satellite estimates can identify days with high risk of exceeding NO₂ ground level thresholds, enabling the creation of risk maps for areas lacking ground sensors. Such results could profoundly impact air quality monitoring in major towns located in valleys of mountain areas. Peak concentrations that deviate from average cycles have also been quantified. These deviations will be compared with other locations characterized by simpler topography to gain a deeper understanding of the limitations of satellite estimates. Subsequently, the next phase involves integrating these models into Machine Learning Algorithms to expand the application of Sentinel 5P data to complex terrain areas. This study is supported by the project “Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE-SIP PYRENEES4CLIMA)”.

How to cite: Campos, C., Sola, Y., Udina, M., Bech, J., and Trapero, L.: Monitoring ground level nitrogen dioxide concentration in complex terrain areas using satellite Sentinel 5P total column observations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-669, https://doi.org/10.5194/ems2024-669, 2024.

The accurate estimation of Surface Solar Irradiance (SSI) is crucial in domains as diverse as climatology, solar energy, agriculture, and architecture. Traditional SSI estimation methods are primarily based on physical models and cloud-index models. These approaches rely on the Independent Pixel Approximation (IPA) and neglect the intricate inter-pixel interactions, 3D effects of clouds, or parallax effects. This reliance on IPA and oversight of spatial dynamics could introduce limitations to traditional methods. These limitations are expected to increase with the advent of third-generation geostationary satellites like the GOES series, which offer enhanced spatial resolution. This work introduces a deep learning framework leveraging the increased spectral, spatial, and temporal resolution offered by third-generation geostationary satellites, without IPA, to improve SSI estimation.

We developed a method using convolutional neural networks (CNNs) to analyze large satellite imagery, high-dimensional in spatial, spectral, and temporal domains, using contextual and multispectral image for SSI estimation. A comprehensive dataset, combining GOES-16 satellite imagery with 5-min global horizontal irradiance (GHI) in-situ measurements from 31 pyranometric stations in the U.S.A. over three years, was constructed and used for model training and validation, allowing for a direct comparison with PSM3, a state-of-the-art physical SSI-satellite-retrieval model from NREL. Our approach combines CNNs for image analysis and fully connected neural networks (FCNs) for processing tabular auxiliary data such as solar angles and positions, exploring various data fusion techniques. We thoroughly assess the model performance using a broad set of metrics, across various conditions and test stations, as well as the influence of varying image sizes on performance.

Results demonstrate the potential of deep learning to outperform traditional models like PSM3 with traditional comparison metrics, especially under cloudy conditions, showing a 25% RMSE improvement. Our analysis highlights the importance of spatial context and the influence of image size in model performance, challenging the adequacy of IPA in traditional methods. A significant improvement is the effect of rotating input images, which substantially enhanced test performance and spatial generalization.

For 5-min GHI estimation, our models achieved a test RMSE of 80 W/m^2, compared to 97 W/m^2 for PSM3, and demonstrated their robustness across diverse evaluation metrics, in most test stations and under various sky conditions. However, the mixed performance in MBE across all sky conditions, as well as other metrics under clear sky conditions and at specific test stations, indicate areas for further improvements in the representativity of the underlying physical process of SSI.

While initial results are promising, further research is needed to refine model architectures and enhance generalization capabilities across different geographical locations Exploring physically informed and probabilistic deep learning methods could be a valuable direction for future research to enhance the spatial generalization, reliability, and interpretability of SSI estimation with deep learning.

How to cite: Becquet, V., Verbois, H., Blanc, P., and Saint-Drenan, Y.-M.: Leveraging Deep-Learning Approaches with Spatial Context for Enhanced Surface Solar Irradiance Estimation from Third-Generation Geostationary Satellite Imagery, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-878, https://doi.org/10.5194/ems2024-878, 2024.

Nowadays, the number of operational renewable energy plants is steadily increasing. It is necessary to analyse how climate change might affect their energy production.  Motivated by this need, this study analyses the Mutriku Wave Energy Plant, which has been operating since July 2011 and has a total capacity of 296 kW.

ECMWF Reanalysis v5 (ERA5) data and Commonwealth Scientific and Industrial Research Organisation (CSIRO) wave climate projections are used between 2015 and 2100. The projections are derived from two CMIP6 models: ACCESS-CM2 and EC-EARTH3, along with their respective parameterizations CDFAC1.08 and CDFAC1. Specifically, we employ six different climate projections, two based on the low-emission SSP1-2.6 scenario and four based on the higher-emission SSP5-8.5 scenario.

Bias correction for vector-valued variables are performed using the multivariate bias technique based on the MBC N-pdf bivariate pdf, while for univariate variables; the classical Quantile Mapping (QM) technique is applied. Additionally, the self-organising map, SOM, technique is also used to classify the daily frequencies and powers of sea type. Finally, the Smirnov test is employed to assess if the shapes of the probability distributions from the different datasets differed statistically. A significance level of 0.05 is used.

Our results show that wave energy production in the Bay of Biscay remains stable in the 21st century. This stability is reflected in a significantly way in the analysis of sea type frequencies and in the energy production. This consistent wave scenario ensures reliable and predictable energy generation, making the Bay of Biscay a valuable source of renewable energy both now and in the future.

How to cite: Carreno-Madinabeitia, S., Serras, P., Ibarra-Berastegui, G., Sáenz, J., and Ulazia, A.: CMIP6 climate projections based wave energy production analysis for Mutriku Wave Energy Plant in the XXI century, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-441, https://doi.org/10.5194/ems2024-441, 2024.

Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas (<40%) were located in the mountainous areas of eastern Liaoning and eastern Jilin. The joint probability and joint recurrence period for agricultural drought events of varying severity were quite different, with the probability following the order light drought > moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.

How to cite: zhang, S.: Identification and Risk Characteristics of Agricultural Drought Disaster Events Based on the Copula Function in Northeast China, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-46, https://doi.org/10.5194/ems2024-46, 2024.

In order to achieve sustainable and optimised grape production within vineyard plots, it is crucial to have a deep understanding of the spatial variability of microclimates. This study utilises a microclimate model (NicheMapR) in conjunction with multiple climate data sources to analyse microclimatic conditions in two vineyard plots: Quinta do Bomfim in northern Portugal and Herdade do Esporão in southern Portugal. The innovative approach achieves a spatial resolution of 10 meters for climate variables. Local station hourly data is combined with ERA5-land data using quantile mapping bias correction. The microclimate model output is further used to correct biases in a EURO-CORDEX model ensemble. Specific climate extreme and bioclimatic indices designed to viticulture are computed for each vineyard plot. By conducting analysis at the 10-meter scale, it becomes possible to identify any potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables that are relevant to grape cultivation within each individual plot. In regions with complex topography, the importance of microclimate analysis is highlighted, as there are significant variations in climatic variables. However, in areas with gentle slopes, the differences in climatic variables are minimal and therefore the significance of microclimate analysis is less pronounced. According to the projections, it is estimated that there will be a median temperature increase of around 3.5°C and 3.6°C in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing future scenarios for the periods 2071–2100 and 1981–2010. Additionally, there is expected to be a decrease in precipitation of approximately 98 mm and 105 mm in these areas. Thus, this study provides a comprehensive and forward-looking approach to analysing microclimates in vineyard plots. By integrating geospatial data, ERA5-land data, and the microclimate NicheMapR model, the research aims to provide viticulturists with a better understanding of current microclimates and future climate scenarios.

 Acknowledgments: Research funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020). Vine & Wine Portugal—Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011.

How to cite: Fonseca, A., Cruz, J., Fraga, H., Andrade, C., Valente, J., Alves, F., Neto, A., Flores, R., and Santos, J.: Towards Resilient Viticulture: Vineyard Microclimatic Zoning as a Tool for Sustainable Adaptation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-138, https://doi.org/10.5194/ems2024-138, 2024.

The Meteorological Service of Catalonia (SMC) has been carrying out phenological monitoring of crops since 2013 to study the impact of climate change on the ecosystem. For this purpose, it was created the Phenological Network of Catalonia (Fenocat), which is made up of eighty observers who monitor the main phenophases of fruit trees such as almond, cherry, olive, apple, orange, or vine, as well as annual winter crops as wheat or barley, annual summer crops as maize or sunflower, and forage herbs such as alfalfa. In addition, 14 birds and 6 butterflies are also monitored.

The Fenocat is a citizen science network whose observers report data two or three times per week, carrying out a periodic assessment of the status of the phenophases for a plant, rather than simply recording the date of an event. This observation system allows the recording of second occurrences in the same season or year (second flowering, second leaf sprouting, second fruit formation), events that happen more frequently due to climate change. The observers insert the information directly into the database using a web application in situ; the web application lets them compare their observations with those produced by other observers.

Fenocat uses BBCH codification and is a data provider of the Pan-European Phenology Database (PEP725).

As genetic factors determine the phenological response, it was carried out a cultivar identification using genetic markers (DNA extraction, Genotyping using molecular markers -SSRs or SNPs- and a calculation of the similarity between the analysed samples and the database information of the Centre for Research in Agricultural Genomics).

After ten years of observations and more than 1,150,000 data collected, we observe a general advance in phenophases such as bud burst, full flowering and maturation, and a delay in the leave fall. For example, in the specific case of the almond tree, the sprouting of the first leaves has advanced 13 days in 10 years, full flowering has advanced by 9 days in 10 years, and the maturation of the almond happens 23 days earlier than 10 years ago (average data for all of Catalonia).

How to cite: Busto, M., Cunillera, J., and de Yzaguirre, X.: Ten years monitoring crops with citizen science, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-282, https://doi.org/10.5194/ems2024-282, 2024.

Evapotranspiration is an essential element in the water cycle, being estimated through various methods such as the Penman-Monteith equation, the Priestley-Taylor equation, the FAO-56 Penman-Monteith equation, the Surface Energy Balance Algorithm etc. With the advent of satellite imagery, constant efforts have been made to improve the spatial resolution of ET estimates through the integration of high-resolution remote sensing data. Evapotranspiration estimated based on MODIS data utilizes the Penman-Monteith equation, resulting in a product at a resolution of 500 meters over a period of over 20 years (2001-2023). Recently, new estimation tools for ET based on Sentinel 2 and Sentinel 3 data have been developed, such as Sen-ET, which uses the Two-Source Energy Balance (TSEB-PT) model and a Data Mining model to bring the product to a resolution of 20 meters. Furthermore, Python libraries developed for the Google Earth Engine platform provide the capability to estimate ET at 30 meters based on Landsat data using the Surface Energy Balance Algorithm. In this study, a comparison of ET will be performed, based on products derived from the integration of satellite imagery obtained from Sentinel 2 and Sentinel 3, Sentinel 2 and Landsat 8, Landsat 8 alone, as well as the ET product from MODIS. The analysis extends over a period of 8 years between 2016 and 2023, which is the common timeframe for all datasets. The study area is located in southeastern Romania, where extensive agricultural fields frequently suffer from drought. The results obtained from satellite imagery will be compared with those obtained from meteorological data, as well as with other satellite products capable of highlighting moisture deficits or drought.

Acknowledgments: This study has received funding from the European Union Agency for the Space Programme under the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101082189 (MAGDA project).

How to cite: Ontel, I., Angearu, C.-V., Irimescu, A., Chitu, Z., and Irasoc, A.: A comparison of remote sensing evapotranspiration products over agriculture crops, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-748, https://doi.org/10.5194/ems2024-748, 2024.

The concept of frost safety margins has been used in previous research as an indicator associated with the period between the occurrence of the meteorological phenomenon and the most sensitive phenological stage to freeze damage in deciduous perennial plants in mid and high latitudes. It is a relevant indicator in agriculture due to its use in agricultural planning and territorial management of productive areas, as it has been linked to damages from late frosts and as a fundamental criterion for false springs events in fruit trees. However, its implementation requires robust phenological records or experimentation under controlled conditions. The aim of this study is to propose a concept of frost safety margins based on daily minimum temperature data (TMIN). To this end, we used TMIN in raster format at a spatial resolution of 1 km² for the Aragón region (northeast Spain). Calculations were performed in the R programming language and the cartography was built using ArcMap 10.7.1® software. The indicator represents a state composed of two elements: a numerical one, without hierarchical order, which incorporates the moment of occurrence; and a character one associated with the difference in days between the penultimate and last frost per year. Once the states were classified, an annual sequence was formed for the historical series and analyzed as a Markov chain. The results show that the margins are persistent and their autocorrelation with lag-1 is statistically significant, especially with increasing altitude. It was also found that, with a greater number of types of margins, the forecast using Markov chains is lower, indicating a more random behavior. In much of the Aragonese territory, the stationary probability of safety margins corresponds to type 4a, i.e., a margin during the month of April with a difference between 1 to 7 days, while in the lowlands it corresponds to type 2a, a margin during the month of March with a difference between 1 to 7 days. The proposed safety margins indicator can be used in conjunction with empirical or modeled phenological data to obtain agroclimatic risk, making it a line of research that allows for expansion with other future research lines.

How to cite: Pérez-Sosa, E., Serrano-Notivoli, R., Saz-Sánchez, M. Á., and Hernández-Navarro, M. L.: Frost safety margins: a proposal based on daily minimum temperature , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-795, https://doi.org/10.5194/ems2024-795, 2024.

Recent advancements in satellite phenology research demonstrate its effectiveness as a robust tool for monitoring vegetation and its responses to climate drivers. Satellite data provides insights distinct from ground-based observations, yet both sources complement each other well. Utilizing new-generation satellites, with high spatial and temporal resolution, facilitates a deeper understanding of climate-phenology relationships. These satellite-derived indices prove especially valuable in regions with scarce ground-based observations like Poland.

Air temperature plays a crucial role in driving various plant developmental processes, among which the initiation of spring activity is the most prominent. However, drawing ultimate conclusions regarding this relationship remains challenging as quantitative estimates strongly diverge. Recent observations indicate that climate warming has altered plant phenology across many European regions. Investigating these complex connections is crucial, given that changes in plant phenology affect fundamental ecosystem functions, including water, carbon, and energy fluxes, as well as interactions between plants and animals, ultimately shaping ecosystem productivity.

The main aim of this study is to evaluate the impact of air temperature conditions on the onset of spring activity of silver birch (Betula pendula) in Poland over the years 2007-2024. The phenology data used in the study include both ground observations of leaf unfolding and satellite-derived estimates of start-of-season (SOS) for silver birch, which is among one of the most abundant deciduous tree species in Poland. Ground-based data were sourced from the Institute of Meteorology and Water Management – National Research Institute and spans years 2007-2024, while the satellite-based SOS metric was derived from Sentinel-2 imagery and covers the 2018-2024 period. The SOS metric was based on EVI (Enhanced Vegetation Index) derivatives. Temperature conditions during individual seasons were assessed using in-situ measurements as well as the E-OBS (v28.0e) gridded dataset, and the relationship between air temperature and the start of the season was studied using temperature anomalies and the Growing Degree Days (GDD) index for 5°C base temperature.

The study showed large inter-annual variability in silver birch phenology – the estimated SOS varied between studied seasons by more than 20 days. The results also proved the usefulness of Sentinel-2 data in monitoring the phenology of deciduous tree species, while indicating that satellite-based estimators pertain to a slightly different phenological phase than ground-based observations. The study provided insights into the links between temperature conditions and the spring phenology of birch, showing that responses to air temperature are complex. The relationship remains evident, as, for instance, in 2018, characterized by extremely warm April and May, an advanced start of the season was observed. Conversely, in 2021, a delayed start of the season was noted as a consequence of cold temperatures during these months. However, quantitatively describing these relationships remains a challenge and further research is necessary.

How to cite: Sulikowska, A., Grabska-Szwagrzyk, E., and Wypych, A.: Satellite-based springtime phenology of silver birch (Betula pendula) in Poland and its relations to air temperature , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-951, https://doi.org/10.5194/ems2024-951, 2024.

Population exposure to hot weather and heat waves endangers human liveability and survivability. The assessment of heat exposure spatiotemporal patterns in both recent past and future can provide essential guidance for targeted adaptation measures. However, existing studies on the topic focus only on environmental heat loads, using single meteorological variables (e.g. air temperature) or simple composite indices (e.g. heat index) to define heat. This diminishes the value of human physiological and behavioral responses to heat. Here, we extend the heat exposure concept to account for these factors and their variability among different population subsets when characterizing heat. For this purpose, we employ a novel, open-access 30-year (1991-2020) human thermal bioclimate dataset that includes hourly values of mPET (modified physiologically equivalent temperature) for diverse populations (https://doi.org/10.5281/zenodo.10893914). We focus on the Athens Urban Area (AUA), which consists of five regional units at the local administrative level, and it is situated in the eastern Mediterranean climate change hot spot. At a first stage, we analyze the long-term trends of acclimatization-based strong heat stress (accliSHS) experienced by male and female adults and seniors in the five AUA regional units. This stage of analysis accounts for accliSHS duration and frequency, as these factors are key in relation to adverse heat-related health outcomes. Then, we combine the mPET estimates with population data for each targeted group and regional unit to compute accliSHS exposure and assess its long-term trends. At this stage, contributions arising for variations in accliSHS and population sizes are decoupled and discussed, focusing on the differences between the examined populations and regional units in AUA. This work is conducted in the framework of the HEAT-ALARM research project and provides valuable insights with respect to population heat exposure for diverse groups of people under a comprehensive human-biometeorological context.

How to cite: Galanaki, E., Agathangelidis, I., and Giannaros, C.: Assessing the exposure of diverse populations to heat under a thermo-physiologically consistent approach in a large Mediterranean urban area, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-297, https://doi.org/10.5194/ems2024-297, 2024.

Tourism has grown in recent years and now involves one sixth of the world's population. However, its dynamics are subject to many economic, social, political and environmental factors. Although the role of weather in the tourist experience is widely recognised, this issue has become increasingly important in recent years as the sector faces the impacts of anthropogenic climate change. Tourist perceptions are important in understanding these impacts. Ratings and reviews shared on websites, social networks and tourism comparison platforms are a useful way of understanding the impact of weather and climate on public perceptions and individual satisfaction.

The aim of this study is to investigate tourists' weather preferences in three of the most visited tourist facilities in Cantabria (northern Spain). First, all TripAdvisor reviews for these facilities were systematically downloaded using automated web scraping techniques. Next, all comments containing information or references were filtered using libraries of weather and climate terms. Finally, the comments were categorised according to the conditions expressed.

Tourists visiting the Cabárceno Nature Park prefer cooler and cloudier days. As most of the activities take place outdoors and usually last several hours, they need to protect themselves from the heat and high levels of radiation. Opinions on rainfall are divided between those who consider it a negative factor and those who appreciate it, while most visitors consider wind to be a negative factor. On the other hand, tourists prefer clear skies at Fuente Dé, as it is a well-known mountain viewpoint. In addition, high temperatures are considered a positive factor almost all year round, while low temperatures are avoided. Rain and wind are generally perceived as negative factors, but usually only for short treks. In Altamira, the influence of the weather is much less obvious, and the only meteor mentioned is rain, but there is no consensus, since for some people rainy days are ideal for visiting the museum, if they don't have to wait in the rain and get wet.

The perception of the weather and its influence on tourism is important and must be considered in the coming years. However, the analysis must be carried out separately for each destination and each type of tourism. Nevertheless, it should be borne in mind that opinions may differ and be partial and therefore need to be critically analysed and combined with local surveys and frequentation data.

How to cite: Conde-Oria, F., Rasilla, D. F., and Toribio Pérez, M.: Analysis of tourists' perceptions regarding the weather influence in Cantabria based on TripAdvisor reviews, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-469, https://doi.org/10.5194/ems2024-469, 2024.

Climate change is highly likely to increase both the frequency and severity of heatwaves, posing a significant challenge to global health. Heatwaves are associated with an increased risk of human mortality, particularly during extreme events that exceed local acclimatization thresholds. However, the impact of heatwaves is not uniformly distributed across all populations. Certain demographic groups, especially the elderly and children, are at a higher risk due to physiological and socioeconomic factors. Moreover, the literature indicates that environmental and socioeconomic factors, such as access to green spaces and income level, also play a crucial role in determining vulnerability to heatwaves. These factors are known to affect thermal comfort, thereby influencing the ability of individuals to cope with extreme heat. Therefore, to mitigate heat-related health outcomes, it is necessary to evaluate the heat vulnerability of districts in Prague and identify where mitigation measures and interventions are most urgently required. 

We use geographically weighted principal components analysis (GWPCA) to determine the role of meteorological (mean summer air temperature, mean number of heatwave days), environmental (proportion and type of greenery, urban coverage ratio), and socioeconomic factors (demographic structure, unemployment rate, mean income) on heat vulnerability (daily heat-related mortality and ambulance call outs spanning the period 2001–2023) in 22 districts of Prague, Czechia. To investigate the influence of air temperature, we utilize a novel ALADIN/CLIMATE-CZ reanalysis with high spatial resolution (2.3 x 2.3 km). Finally, a multivariate meta-regression model is used to determine the risk of mortality and ambulance call-outs associated with high temperature in each district, taking into account the modifying effect of the district characteristics (i.e., principal components from the GWPCA).

Our findings will provide new insights to the heat vulnerability assessment in Prague and will enable us to determine the most at-risk areas of Prague regarding its population structure and environmental conditions. The study contributes to a comprehensive understanding of the underlying drivers of heat vulnerability in Prague and informs targeted interventions to mitigate the impacts of extreme heat on vulnerable populations.

How to cite: Dogan, T. and Urban, A.: Socioeconomic and Environmental Determinants of Heat Vulnerability in Prague, Czechia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-793, https://doi.org/10.5194/ems2024-793, 2024.

Since the restoration of the Meteorological Service of Catalonia (SMC) in 2002, data rescue, quality control and homogeneity analysis of climate series have been one of the main focuses of the Climatology Area ofSMC. Working with continuous series, having a wide temporal coverage and ensuring good spatial density are essential for accurately characterizing climate change and variability.

To achieve this goal, significant effort must be invested in data preservation and rescue, identifying meteorological series of interest, and ensuring good digitization, transcription and cataloging. This work describes the process of data rescue at SMC, from identifying meteorological significant collections, to entering all data into the historical base of SMC, while preserving original documentation.

Collections with meteorological documentation vary widely.

• Historical newspapers. In recent years many state, provincial and local archives in Catalonia have tried to digitize historic newspapers.SMC has identified over 175 periodicals containing meteorological observations and metadata information.
• Private archives. Several private archives, both religious and corporate, have been found to contain documentation related to meteorological observations.
• Observers or their relatives who have preserved the original documentation, contact SMC for the preservation.

When a collection arrives at SMC, there are essentially four possibilities. (1) First, it may involve a temporary transfer of documentation, in which case it is digitized and returned to the owner. (2) Alternatively, if photography of the documentation is not permitted, it may be necessary to visit the location and transcribe the data. (3) A third option is that, once the documentation is digitized, it is restored if necessary and then entered into the National Archive of Catalonia through an agreement with SMC to ensure optimal preservation conditions. (4) Finally, data may be provided directly by the observer (already digitized or not), which can then be entered directly into the historical database.

Once the data is extracted, any existing gaps in the historical database are filled, and coverage periods of the stations are extended. Data from previously unpublished stations are also incorporated, enriching and enhancing our ability to monitor variability and climate change in Catalonia.

How to cite: Herrero-Anaya, M., de Yzaguirre, X., Prohom, M., Cunillera, J., Barrera, T., Ruiz, A., and Montserrat, J.: 20 years of data rescue initiatives at the Meteorological Service of Catalonia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-305, https://doi.org/10.5194/ems2024-305, 2024.

How to cite: Healion, K., Thorne, P., Noone, S., Andersson, A., van der Schrier, G., McKinstry, A., and Poli, P.: The Copernicus Climate Change Service’s data rescue and surface meterological data collection effort , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-331, https://doi.org/10.5194/ems2024-331, 2024.

Homogenisation and interpolation of bright sunshine duration time series in Slovenia have recently been renewed at Slovenian Environment Agency (ARSO). First, the quality of measured hourly to daily data at meteorological stations for the period 1960–2022 was checked. QC data was then aggregated to monthly sums, obtaining 27 stations’ time series, separately for three subdaily intervals. Monthly data was normalized relative to the maximum possible sunshine duration and transformed by arcus sinus function to improve the homogenisation with the HOMER software tool. Homogenisation was run in several iterations, using metadata and applying additive correction model with seasonally dependent corrections. Resulting homogenised time series were then back-transformed to the measurement units (hours).

Original subdaily values of sunshine duration were adjusted to match the monthly values of homogenised data. The first step of the adjustment procedure was the normalization of the subdaily data (i.e. division with the maximum possible sunshine duration for the day of year). Resulting ratio values from 0 to 1 were transformed by arcus sinus functions and then shifted by such a value that the monthly sum of back-transformed values matched the homogenised monthly sum. Shifted values below –π/2 were set to –π/2, whereas values above π/2 were set to π/2. This way all the original ratio values of 0 (no sunshine) and 1 (maximum possible sunshine) remain unchanged. This adjustment procedure takes into account both the sunshine duration frequency distribution and the influence of expected inhomogenities.

Missing subdaily data was replaced by spatially interpolated values of homogenised daily ratios. The distribution of raw interpolated values was partly adjusted to the distribution of the corresponding homogenised data in order to improve the statistics of extreme values.

The resulting collection of homogenised and interpolated subdaily data of bright sunshine duration was statistically analysed. Time series of the data show a statistically significant positive linear trend on an annual scale throughout the 63-year period. The trend is stronger for morning (around 3.7 % per decade) than for midday (2.0 % per decade) and afternoon values (2.5 % per decade). For autumn the trend is weak and insignificant, whereas for winter morning hours the trend reaches around 8 % per decade in the lowlands of eastern Slovenia.

Homogenised an interpolated time series have been used for the calculation of climate normals for the latest WMO standard reference period (1991–2020) and are planned to be used to improve climate projections for Slovenia.

Keywords: sunshine duration, climate change, homogenisation, spatial interpolation

How to cite: Vertačnik, G.: Homogenisation and interpolation of subdaily bright sunshine duration time series in Slovenia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-340, https://doi.org/10.5194/ems2024-340, 2024.

Reliable air quality data are vital for informed decision-making, enabling evidence-based mitigation strategies to improve public health and sustainability. Although monitoring stations are essential for assessing air quality, they have limited spatial representativeness, leaving large extensions of areas without appropriate observational data. On the other hand, numerical air quality systems provide full spatial coverage. Nevertheless, modeled data are affected by persistent uncertainties, mainly due to emission inventories inaccuracies and the complexity of atmospheric processes involved in pollution transport. Data-fusion methods offer bias-corrected air quality maps with full spatial coverage. There is, however, a strong dependence on observational data availability to ensure reliable results of data-fusion methods. 

In this study, we quantify the impact of imputing missing observational data in data-fusion methods. We focus on PM2.5 for the region of Catalonia (Northeastern Spain) during 2019, for which data availability is strongly limited. We first present straightforward gap-filling methodologies, such as linear interpolation and persistence (repetition of the previous available value). We then compare these techniques with a state-of-the-art artificial intelligence gap-filling method based on the Gradient Boosting Machine algorithm trained with several years of data (2019-2022). To assess gap-filling methodologies, we generate random gaps of varying characteristics identifying the optimal technique for each gap size and frequency. Finally, we study how these methods affect the data-fusion process applied to the mesoscale air quality model CALIOPE. The output of this system has a horizontal spatial resolution of 1 km x 1 km on a daily scale. The data-fusion method uses universal kriging, a geostatistical technique based on a regression model and the spatial correlation between the model and observational data. 

Data-fusion results show significant improvement when using gap-filling observational data. Notably, the method’s effectiveness depends on observation availability, performing better with GBM-filled data.

How to cite: Barrantes, A., Carnerero, C., and Mateu Armengol, J.: The role of gap-filling observational data in air quality data-fusion methods: a case study with CALIOPE, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-768, https://doi.org/10.5194/ems2024-768, 2024.

Access to spatially comprehensive information of climate variables spanning multiple decades is crucial for various applications, including ecosystem modelling, climate monitoring, and the evaluation of climate models. However, existing observational temperature datasets for climate monitoring in Austria often exhibit limitations in either temporal extension or spatial comprehensiveness. The HISTALP dataset provides homogenized monthly observation series of air temperature for the greater Alpine region, with records dating back to the 19^th or even 18^th century, but with limited spatial coverage. Conversely, the Austrian spatial climate observation dataset SPARTACUS offers daily-resolved high-resolution spatial grids of air temperature but is restricted to the period after 1961.

This study aimed to address these limitations by constructing a temporally consistent grid dataset of monthly air temperature for Austria, covering the period from 1781 to 2020. Combining the strengths of both the HISTALP and SPARTACUS datasets, we applied a statistical reconstruction technique called „Reduced Space Optimal Interpolation“ (RSOI), involving a Principal Component Analysis (PCA) and Optimal Interpolation (OI). This methodology allowed us to merge long-term, continuous, and homogeneous mean air temperature series from HISTALP with the high-resolution grids derived from SPARTACUS. A further advantage of this method is the possibility to reconstruct the temperature evolution during the early instrumental period even in regions where direct observations were lacking at that time.

The resulting grid dataset, named SOCRATES (Spatial Reconstruction of Climate in Austria Combining SPARTACUS and HISTALP Datasets), provides monthly grids of air temperature anomalies back to 1781 with respect to the reference period 1961-1990. These anomaly grids allow the derivation of absolute temperature grids as well as seasonal and annual aggregates. Beside details on the method, we will present some results of the evaluation. The comparison of the reconstruction with observations by applying a leave-one-out cross validation showed a bias close to zero across all reconstruction periods and seasons. The mean absolute error (MAE) decreased over the considered reconstruction periods, i.e. from 0.35 K for 1781-2020 to 0.22 K for 1951-2020, regarding full years. Furthermore, the MAE showed a seasonal dependence with the lowest errors in summer and highest errors in winter. The applicability of the reconstructions further depends on the regions within Austria. In low-lying parts of northern and eastern Austria, the results demonstrated high reconstruction skill, even for the earliest reconstruction period, while for southern Austria and high elevations it is recommended to consider reconstruction periods starting in 1851 or later. Overall, the results emphasized the capability of SOCRATES in achieving high temporal consistency, which is essential for its use in the long-term spatial climate monitoring in Austria.

How to cite: Rohrböck, A., Hiebl, J., Isotta, F., and Tilg, A.-M.: Reconstruction of long-term consistent air temperature grids for Austria back to 1781, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-411, https://doi.org/10.5194/ems2024-411, 2024.

This study investigates the refinement of short-term precipitation interpolation, focusing on regions prone to intense, localized thunderstorms like supercells. Traditional meteorological stations often miss these events due to their limited spatial coverage, leaving significant precipitation accumulations unrecorded, leading to incomplete representations and errors in interpolation. To mitigate these interpolation errors, auxiliary data sources such as satellite imagery, weather forecasts, and radar measurements are crucial for refining interpolation processes and enhancing our understanding of precipitation patterns. In our research we integrate radar background information into the MISH (Meteorological Interpolation based on Surface Homogenized Data) method as documented in the studies authored by Szentimrey and Bihari (2007, 2014).

Using the MISH method, we processed 10-minute precipitation datasets with and without 10-minute radar-derived background information across the study area building on our prior research (Bokros et al., 2023). We examined how MISH handles radar anomalies, including errors, missing data, and spurious measurements from unintended reflections.

Statistical techniques were employed to elucidate the extent to which the inclusion of radar-derived data enhanced the quality of interpolation. Furthermore, our investigation aimed to quantify the robustness of the relationship between interpolations conducted with radar-derived background information and those performed without such supplementary data.

Integrating radar-derived background information into interpolation processes is essential for improving societal resilience, agricultural productivity, and hazard forecasting accuracy in areas susceptible to intense thunderstorms. This improvement can lead to better preparedness and mitigation strategies.

The research was conducted within the framework of the Széchenyi Plan Plus program, with support from the RRF 2.3.1 21 2022 00008 project.

How to cite: Bokros, K., Izsák, B., Lakatos, M., and Szentes, O.: Enhancing precision in short-term precipitation interpolation with radar background: unraveling case studies through 10-minute radar data analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-421, https://doi.org/10.5194/ems2024-421, 2024.

High-resolution gridded climate data derived from in-situ observations play a crucial role in global and regional climatology. The data are a valuable input for further climate impact studies, especially in ecological and energy modeling, and can be subsequently used for the wind power potential analysis.
Moreover, policymakers can make informed decisions based on accurate climate information derived from these datasets, enhancing the effectiveness of climate-related policies and interventions.

This study explores methods to enhance mean wind speed interpolation techniques over complex topography, resulting in the creation of a high-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023. A two-step approach is tested, wherein climatologies for each month are computed using the best-performing interpolation technique. Subsequently, the optimal interpolation method would be employed to interpolate the model residuals (in case of machine learning (ML) superiority). In the subsequent stage, the same interpolation approach is applied to interpolate daily residuals to the monthly climatologies. Combining both fields produces the final gridded daily mean wind speed dataset.

Various spatial interpolation approaches, including Inverse Distance Weighting (IDW), 3D IDW (an Euclidian method, which accounts for elevation differences), Thin Plate Splines (tp_spline),  Local Polynomial Interpolation (loc_poly), and Kriging approaches (OK, OK_trend, UK, UK_poly) are evaluated. Additionally, the results would be compared to regression models, such as Ridge Regression (RR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Gradient Boosting Regression (GBR),  as well as ensembles of these models by combining different regressors in a pipeline. Each selected regression model is trained independently on the training data, and the final prediction is obtained by averaging the individual model predictions. Each model has the same set of predictors and is set up for each month separately.

Additionally, qualitative comparisons will be conducted with other high-resolution gridded datasets. The dataset will be made publicly available for download.

How to cite: Klisho, T., Lehner, F., Laimighofer, J., and Formayer, H.: High-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-455, https://doi.org/10.5194/ems2024-455, 2024.

As we acknowledge the increasing frequency, broader distribution, and heightened intensity of extreme weather events due to climate change, it becomes crucial to gain a deeper understanding of their behaviour. These extreme events have significant societal impacts, requiring better knowledge and tools for assessment.

The Climate Atlas of Extremes in Catalonia, covering the period from 1991 to 2020, serves as a vital reference tool. It provides valuable information to government bodies, regional authorities, businesses, and citizens about the specific characteristics of climate extremes in Catalonia over the past three decades.

In this study, we present the methodology behind the creation of the Catalonia Climate Extremes Atlas. Our goal is to provide a comprehensive analysis of extreme climate events in the Catalonia territory. We will present the key steps involved in this process.

• Data Preparation: we collect and preprocess daily climate data, including mean, maximum, and minimum temperatures, as well as precipitation records. Rigorous quality control procedures are applied to ensure data accuracy. Homogeneity analysis: artificial biases in the data series are addressed using the ACMANTv5 method. This step ensures that the dataset accurately represents the true climate conditions. To obtain descriptive indices to evaluate extreme events, we utilize the CLIMPACT tool (https://climpact-sci.org/indices/). These indices were defined by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) and provide valuable insights into extreme weather phenomena.
• Digital cartography: we interpolate extreme normals to create georeferenced climate data on a regular grid. The resulting high-resolution digital cartography includes precipitation indices mapped at a 1-kilometer resolution, and temperature indices mapped at a 100-meter resolution. Different time scales (monthly, seasonal, and annual) are considered based on relevance. The extreme normals will be freely accessible through the SIG portal of the Government of Catalonia (https://sig.gencat.cat/visors/hipermapa.html).

Ongoing research involves trend calculations and statistical approaches. We will compare different periods of extremes and assess changes. Additionally, we plan to compare our findings with model-projected changes in the same extremes.

How to cite: Barnolas, M., Barrera-Escoda, A., Prohom, M. J., and Serra, A.: Climate Atlas of extremes in Catalonia (1991-2020) , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-745, https://doi.org/10.5194/ems2024-745, 2024.

How to cite: Albalat, A., Trapero, L., Lemus-Canovas, M., and Pons, M.: Characterizing snow seasons through the application of a new Multivariate Snow Index , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-429, https://doi.org/10.5194/ems2024-429, 2024.

ClimRisk.eu is a new web portal about climate change projections and underlying data needed for climate proofing. ClimRisk.eu is designed for investors planning their investments to withstand the future climate conditions. It is intended to help the public organizations to formulate their policies and plan specific adaptation measures. It is also a tool for companies and private entities to comply with the EU requirements to assess the sustainability of investments (EU Regulation 2021/1060, the so-called EU Taxonomy). ClimRisk.eu also serves as the data source for individuals and broad public and their climate-related personal interests.

ClimRisk.eu works over two domains: Czech Republic and Central Europe. Information for the Czech Republic is based on more detailed and accurate data inputs, e.g. climate observations and it is derived from high resolution data grid of 0.5 km step. The data on the Central European domain is built on the less detailed data sources and thus the spatial resolution reaches only 10 km. Climate projections are delivered for four selected Shared Socioeconomic Pathways (SSP) scenarios and ensemble of seven Coupled Model Intercomparison Project Phase 6 global climate models (GCMs). The set of seven  GCMs was taken from a larger ensemble consisted of more than 20 CMIP6 GCMs and its choice was done to reduce the effort needed to process all data while keeping the statistical properties of the original ensemble of more than 20 GCMs.ClimRisk.eu offers long-term means of meteorological parameters (air temperature, precipitation, wind speed, humidity, solar radiation) as well as important climate indices including those focused on extremes. Information on the range of uncertainty related to climate projection for a given territory is also included at ClimRisk.eu.

Analysis of future climate conditions is based on simulations of the most recent generation of global climate models (GCM). Given the outputs of climate models are associated with systematic errors (due to the necessary simplification of the complex real-world processes), they need to be corrected in order to obtain meaningful results about the simulated properties of the climate system. In case of GCM it is not possible to apply correction methods suitable for RCMs, like quantile mapping. In this case this is made possible by the Advanced Delta Change ("ADC") method.

How to cite: Trnka, M., Stepanek, P., Skalák, P., Balek, J., Zahradníček, P., Meitner, J., and Farda, A.: ClimRisk.eu – new climate proofing tool for Central Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1014, https://doi.org/10.5194/ems2024-1014, 2024.

Ensemble precipitation forecasts with sub-seasonal lead times provide useful information for decision-makers when they sufficiently sample the possible outcomes of trajectories. In this study, we present a forecasting tool for extreme precipitation ensemble forecast over Europe using a stochastic weather generator (SWG) based on analogs of the atmospheric circulation. This approach is tested for sub-seasonal lead times (from 2 to 4 weeks) to forecast European precipitation and temperature as well as the Madden Jullian Oscillation (Krouma et al, 2022,2023). SWG ensemble forecasts yield promising probabilistic skill scores for shorter and sub-seasonal timescales for precipitation (Krouma et al., 2022,2024) as well as for temperature (Yiou and Déandréis, 2019).

An updated version of the SWG, HC-SWG forecasting tool (HC refers to Hindcast and SWG to the stochastic weather generator) based on a combination of dynamical and stochastic models, was used to forecast European precipitation for the sub-seasonal lead time (Krouma et al., 2024, in review, QJRMS). The HC-SWG is based on analogs of the S2S model of the ECMWF and CNRM ensemble members 5 days ahead. We obtained reasonable forecast skill scores at the station level with respect to climatology. And we found that the HC-SWG shows improvement against the ECMWF precipitation forecast until 25 days.

In this work, we aim to use the HC-SWG to generate an ensemble of 100 members for extreme precipitation over Europe at the station level (Stockholm, Madrid, Paris..). We evaluate the ensemble forecast of the HC-SWG and we compare the HC-SWG forecast with other precipitation extreme forecasts to further confirm the advantage of our method.

How to cite: Krouma, M. and Messori, G.: Ensemble forecast of extreme precipitation in Europe by combining a stochastic weather generator with dynamical models , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-28, https://doi.org/10.5194/ems2024-28, 2024.

In disaster prevention, water management, agriculture, and hydropower generation, an accurate seasonal streamflow forecast (SSF) is crucial, while global approaches become necessary in regions lacking forecast systems. This study evaluates the Météo-France seasonal prediction system (SYS8) skill for global SSF through hindcasts of river discharges. Contributing to Copernicus Climate Change Services (C3S), the SYS8 employs a fully coupled Atmosphere-Ocean General Circulation Model (AOGCM) with an advanced river routing component (CTRIP) interacting with the ISBA land-surface scheme. This research is part of the European project CERISE, which aims to enhance the C3S seasonal forecast portfolio by improving land initialisation methodologies.

SYS8 derives land initial conditions from a historical coupled initialisation run where land-river is weakly constrained, while atmosphere/ocean is nudged to the ERA5/GLORYS re-analysis. This study improves the initialisation run by relaxing soil moisture to fields reconstructed from an offline land simulation. Daily streamflow ensemble hindcasts of 25 members are generated in a 0.5° grid, with a lead time of up to 4 months initialised on the 1^st of May/August/November between 1993-2017, allowing hindcasting summer (JJA), fall (SON) and winter (DJF) seasons. Forecast skill is assessed against discharge observations in 1608 monitored basins worldwide (with areas > 3000 km²) using deterministic and probabilistic metrics. The classical Ensemble Streamflow Prediction approach (ESP) is a benchmark for evaluating the control SYS8 skill and the additional skill of moisture nudging.

Globally, the control SYS8 skill is superior to the ESP, but the bias is higher in dry regions such as northeastern Brazil, western US and some rivers in Spain and Africa. On the other hand, the hindcast with enhanced land surface initial conditions outperformed the control SYS8 and benchmark ESP, especially during summer. Local skill degradation in higher latitudes will be discussed. Still, overall positive results support ongoing efforts to enhance land initialisation through a global land data assimilation system.

How to cite: Narváez Campo, G. F. and Ardilouze, C.: Global Streamflow Seasonal Forecast by a novel two-way AOGCM/Land/River coupling, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-158, https://doi.org/10.5194/ems2024-158, 2024.

Being able to predict meteorological droughts several weeks ahead would add value to many sectors including agriculture, river shipping as well as water and energy management. A commonly used meteorological drought index is the standardized precipitation index SPI-N, which puts precipitation anomalies of the past N months into a climatological perspective. The SPI correlates with anomalies of soil-moisture, streamflow or groundwater storage, and thus serves as an inexpensive and attractive hydrological proxy. In this study we quantify how well the SPI-N can be skillfully forecasted in Switzerland. Using ECMWF IFS extended-range forecasts quantile mapped from its native 36 km to a 2 km grid, we produce ensembles of SPI-N forecasts for the Swiss drought warning regions. While previous research has underlined the challenges faced by ensemble forecasting systems in accurately predicting daily precipitation in Europe beyond lead week 1, our analysis reveals that the skill of SPI-1, SPI-3, and SPI-6 forecasts extends into weeks 3 and 4. It generally holds that skill SPI-6 > skill SPI-3 > skill SPI-1. For example, we find that the skill of an SPI-3 forecast for week 4 is comparable to the skill of an SPI-1 forecast for week 2. Overall, the results indicate the potential for skillful prediction of meteorological drought on sub-seasonal timescales. We link the extended predictability horizon to the inherent characteristics of the SPI being a temporal aggregate: the SPI is less sensitive to the exact timing of precipitation events, while also retaining “memory” of past precipitation. The latter manifests in larger skill for longer accumulation time N, in which more observation are weighted into the forecasted SPI. Finally, we show how SPI forecasts and hydrological forecasts are devised as factors for the combined drought indicator, which forms the numerical basis of the new Swiss drought early warning system.  

How to cite: Imamovic, A., Büeler, D., Pyrina, M., Humphrey, V., Spirig, C., and Domeisen, D.: Skillful Extended-Range Forecasts of Standardized Precipitation Indices for Drought Early Warning in Switzerland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-162, https://doi.org/10.5194/ems2024-162, 2024.

Climate Services based on seasonal forecasts are a powerful tool for adaptation in a changing climate and they attract growing interest from different sectors. However, operational seasonal forecasts have traditionally been issued following a subjective procedure, combining information from different sources, such as observation, empirical and dynamical models. Although it adds value by incorporating expert knowledge, the subjective procedure usually results in graphic products, with limited traceability, and not suitable for objective skill assessment or coupling sectoral applications. Identifying this issue, WMO encourages Regional Climate Centers and RCOFs to develop an objective procedure. The purpose is to increase the reliability of our results and to provide the basis for future climate services. With that aim, we explored ways of developing an objective approach that adds value to raw model forecasts in the Mediterranean region.
As is usually accepted, the starting point is a multimodel ensemble, which in our case combines seven Copernicus seasonal forecast models, hoping to minimize the weaknesses of individual models. The work focuses on looking for ways of subsampling the ensemble data based on comparing observational patterns with the evolution of ensemble members at the beginning of the period. Therefore, we did not use the latest model run, choosing instead earlier initializations and applying techniques such as cluster analysis or subsampling a fixed number of members to select those that were closer to reality.
First, we performed a cluster analysis to the ensemble forecast for winter (DJF) 2023-2024 and chose the cluster which best predicted the values of precipitation in October, which would be the last month with complete data when producing the winter seasonal forecast. However, we did not find a significant increase in skill in the Mediterranean region, possibly due to the great differences in cluster population between each year of the hindcast.
Then, we tested an alternative method by selecting a fixed number of members for the forecast and each year of the hindcast. We subsampled the group of members which best predicted precipitation in October and found a significant increase in skill in certain areas. However, there were not consistent improvements along the whole region, with some areas showing lower skill.
A comparison of the methodology using different model runs was conducted, finding better performance for the September run.

How to cite: Pérez Pérez, F. J. and Rodríguez Guisado, E.: Subsampling members in a seasonal forecast ensemble, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1101, https://doi.org/10.5194/ems2024-1101, 2024.

The zonal inhomogeneities of the land surface are known to strongly modulate the large-scale atmospheric circulation, especially in the northern hemisphere. This is related both to the mechanical forcing induced by high orography and to the thermal contrast between water and land. In a warming world, it is known that such a thermal contrast is changing but its impacts on the large-scale atmospheric circulation are poorly constrained. Recently, it has been shown that a reduced winter thermal land-sea contrast is projected to reduce the amplitude of planetary waves, with a predominant control of the Asian-Pacific land-sea contrast at the global scale. 

Goal of the present study is to investigate the impacts of an enhanced summer thermal land-sea contrast on the planetary circulation. In particular, the role of soil moisture in modulating such contrast is considered with special attention. In fact, despite the importance of the land-atmosphere coupling mechanisms, that involve feedbacks with atmospheric circulation, clouds, precipitation and surface fluxes, there are still fundamental gaps in their understanding. These gaps result in an incorrect representation of the global hydrological cycle in CMIP6 models. For example, CMIP6 models are characterized by large biases in the water vapor trend representation in arid and semi-arid regions, which might be related to a poor representation of soil moisture and its impacts on the overlying and downstream atmospheric dynamics.

By analyzing the CMIP6 model spread in soil moisture, surface air temperature and upper-tropospheric geopotential height, we aim to quantify the variability of the planetary circulation in a range of realistic soil moisture configurations. Climate data records of satellite products of surface soil moisture can also be used to constrain the soil moisture variability observed in the last decades. In the second part of the project, numerical simulations with an Earth Model of Intermediate Complexity will shed light on the link between soil moisture distribution, land-sea summer contrast and planetary circulation.

How to cite: Meroni, A. N., D’Andrea, F., and Pasquero, C.: Impacts of the changing summer thermal land-sea contrast on the northern hemisphere planetary circulation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-21, https://doi.org/10.5194/ems2024-21, 2024.

Recent several studies have focused on the predictability of tropical cyclone track forecast. As a response to the question issued by Landsea and Cangialosi (2018) about "the approaching limit of predictability for tropical cyclone (TC) track prediction is near or has already been reached", Zhou and Toth (2020) (short for ZT20) and Yu et al. (2022) (short for Y22) have found that the limit of predictability for TC track prediction has not been reached both in Atlantic (ATL) and Western North Pacific (WNP) basins. However, the predictabilities are different in two basins, as ZT20 found that 1 day's improvement can be obtained through 10 years in ATL, while Y22 found that 2 days' improvement can be obtained through 15 years in WNP. To reveal the causes of this difference, the predictability of TC track in WNP is first investigated under the same framework as ZT20. Then important parameters that determined the predictabilities are found and analyzed. Results suggested that the growth rate of true track forecast error in WNP is higher than that in ATL, indicating a lower predictability in WNP. Further explorations suggested that TCs in WNP basin have averagely larger sizes, stronger intensities, lower-latitude locations, and poorer forecast skills of their guided flows. All these factors contribute to the larger track forecast error growth rate. Moreover, it is pointed out that as the improvement of forecast skills over years mainly due to the reduction of initial analysis errors, although a lower predictability is found in WNP, the forecast skill improvement in WNP is faster than that in ATL.

How to cite: Zhou, F. and Ye, Y.: Reasons for Different Predictability of Tropical Cyclone Tracks in the Western North Pacific and Atlantic Oceans, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-39, https://doi.org/10.5194/ems2024-39, 2024.

Understanding extreme precipitation events (EPEs) and their underlying dynamical processes and moisture transport patterns is essential to mitigate EPE-related risks. In this study, we investigate the dynamics of 82 EPEs (≥ 100 mm∙day^-1) over the territory of Ukraine in the recent decades (1979-2019), of which the majority occurred in summer. The EPEs are identified based on precipitation observations from 215 meteorological stations and posts in Ukraine. The atmospheric variables for the case study analysis of selected EPEs and for climatological composites and trajectory calculations were taken from ERA5 reanalyses. Moisture sources contributing to the EPEs in Ukraine are identified with kinematic backward trajectories and the subsequent application of a moisture source identification scheme based on the humidity mass budget along these trajectories. The large-scale atmospheric circulation associated with EPEs was studied for a selection of representative EPEs in all seasons and with the aid of composites of all events per season. Results show that EPEs in summer occur all across Ukraine, but in other seasons EPE hotspots are mainly in the Carpathians and along the Black and Azov Seas. All EPEs were associated with a surface cyclone, and most with an upper-level trough, except for the winter events that occurred in situations with very strong westerly jets. Isentropic potential vorticity anomalies associated with EPEs in Ukraine show clear dipole structures in all seasons, however, interestingly with a different orientation of these anomaly dipoles between seasons. The analysis of moisture sources revealed a very strong case-to-case variability and often a combination of local and remote sources. Oceanic sources dominate in winter, but land evapotranspiration accounts for 60-80% of the moisture that rains out in EPEs in the other seasons. Taken together, these findings provide novel insight into large-scale characteristics of EPE in Ukraine, in a region with a unique geographical setting and with moisture sources as diverse as Newfoundland, the Azores, the Caspian Sea, and the Arctic Ocean.

How to cite: Agayar, E., Aemisegger, F., Armon, M., Scherrmann, A., and Wernli, H.: Precipitation extremes in Ukraine from 1979 to 2019: Climatology, large-scale flow conditions, and moisture sources, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-88, https://doi.org/10.5194/ems2024-88, 2024.

This study characterizes and contrasts Rossby wave activity during the 25 sudden stratospheric warming (SSW) and 31 strong polar vortex (SPV) events that occurred in the period 1979–2021. While the events are tied to a decrease or increase, respectively, in background flow, it is less clear how the associated properties of Rossby waves change, e.g., how their phase speed is affected. The goal is to identify the specific tropospheric and stratospheric waves exhibiting anomalous behavior during these events. Applying space-time spectral analysis to ERA5 reanalysis data allows us to assess both the wavenumber and the zonal phase speed of the waves.

We find that SSW events are associated with a reduced phase speed of Rossby waves, first in the stratosphere and then in the troposphere, while SPV events are tied to a concomitant increase of phase speed across vertical levels. These phase speed anomalies become significant around the event and persist for 2-3 weeks thereafter. In the stratosphere, both SSW and SPV events are dominated by the change in the background flow, manifested as a systematic reduction or increase, respectively, in eastward propagation of Rossby waves across most wavenumber.

In the troposphere, the effect of the background flow is complemented by changes in wave properties, with a shift towards higher wavenumbers during SSW events and towards lower wavenumbers during SPV events. This opposite response between SSW and SPV events is also visible in the meridional heat and momentum flux co-spectra, which highlight from a novel perspective the connection between stratospheric Rossby waves and upward propagation of waves.

How to cite: Schutte, M., Domeisen, D. I. V., and Riboldi, J.: Opposite spectral properties of Rossby waves during weak and strong stratospheric polar vortex events, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-118, https://doi.org/10.5194/ems2024-118, 2024.

The tropics are the most active regions of convective activity on earth, which plays a crucial role in global atmospheric energy and water cycle. Climate change due to increases of greenhouse gas concentrations has led to a substantial increase in intense convection and extreme precipitation.  We use simulations from a global cloud-system resolving model, Nonhydrostatic Icosahedral Atmosphere Model (NICAM), to study the future changes of extreme convection include overshooting deep convection (ODCs) and the most intense convection (MICs) at the end of the 21st century. Combining TRMM satellite observations with ERA5 reanalysis data, we find that the NICAM well reproduces the spatio-temporal distributions of TRMM observed extreme convection and atmospheric environment. The results show that future extreme convection will show a globally increasing trend with climate warming. However, the trends vary either between different convective property thresholds or between different regions. For example, the future occurrences of ODCs with cloud top height above 15.5 km, 16.9 km and 18.3 km scaled by the global temperature increase will increase by 7%/K, 27%/K and 90%/K, respectively, over ocean where the atmosphere becomes warmer and wetter in a warming world. The corresponding changes are -1%/K, 10%/K and 37%/K over land where the atmosphere is hotter but drier. In the other hand, the frequency of MICs will increase significantly in the Atlantic and central Pacific, while decrease slightly in central and northern Africa. Specifically, the frequency of MICs in the Northern Hemisphere will increase significantly in boreal summer and decrease in boreal winter. Nevertheless, the GCRM simulations also show some discrepancies compare to the observations, i.e., the simulated convection over northeast of the South America was significantly less, which still need a further improvement. However, GCRM will be a crucial tool for studying global climate change and will undoubtedly provide important assistance for us to better address future climate change.

How to cite: Wu, X., Fu, Q., Jia, D., and Kodama, C.: The Future Responses of Tropical Extreme Convection to Climate Change based on GCRM simulation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-209, https://doi.org/10.5194/ems2024-209, 2024.

The North Atlantic Oscillation (NAO) has been confirmed to be closely related to the weather and climate in many regions of the Northern Hemisphere; however, its effect and mechanism upon the formation of dust events (DEs) in China have rarely been discussed. By using the station observation dataset and multi­ reanalysis datasets, it is found that the spring dust aerosols (DAs) in North China (30-40° N, 105- 120° E), a non-dust source region, show high values with a strong interannual variability, and the spring DAs in North China are significantly correlated with the previous winter's NAO. According to the nine spring DEs affected significantly by the negative phase of the preceding winter's NAO in North China during 1980-2020, it is shown that before the outbreak of DEs, due to the transient eddy momentum (heat) convergence (divergence) over the DA source regions, the zonal wind speed increases in the upper-level troposphere, strengthening the zonal wind in the middle-lower levels through momentum downward transmission. Simultaneously, there is transient eddy momentum (heat) divergence (convergence) around the Ural Mountains, which is favorable for the establishment and maintenance of the Ural ridge, as well as the development of the air temperature and vorticity advections. The combined effects of temperature and vorticity advections result in the Siberian Highs and Mongolian cyclone to be established, strengthen, and move southward near the surface, guiding the cold air from high latitudes southward, and is favorable for the uplift and transmission of DAs to North China downstream. Simultaneously, the changes in upstream transient eddy flux transport can cause both energy and mass divergence in North China, resulting in diminishing winds during DEs, which would facilitate the maintenance of dust aerosols here and promote the outbreak of DEs. This study reveals the impact of transient eddy flux transport on the dusty weather anomalies modulated by the NAO negative signal in North China, which deepens the understanding of the formation mechanism of DEs in China.

How to cite: Li, Y. and Li, X.: Influence of the previous North Atlantic Oscillation (NAO) on the spring dust aerosols over North China, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-289, https://doi.org/10.5194/ems2024-289, 2024.

Europe has been identified as a heatwave hotspot, with numerous temperature records having been broken in recent summers. These temperature extremes exact a heavy toll on society; approximately 60,000 heat-related deaths have been estimated for the summer of 2022 alone. With projections showing extreme temperatures becoming more frequent, intense and longer in duration, there is a pressing need to further develop heat-warning systems to help protect, in particular, the most vulnerable members of society. Here we evaluate the skill of daily temperature related mortality forecasts in the context of current numerical weather prediction model capabilities. We consider the summers of 2022 and 2023 as case studies and find that temperature related mortality can, on average, be forecast skilfully up to lead times of approximately one week for these two summers, although we also note the increased predictability in south-western Europe in late-July 2022 coinciding with record breaking temperatures. We further discuss the implications that the non-linear relationship between temperature and temperature related mortality has on temperature related mortality forecast spread and errors, concluding that further developments in forecasting capabilities for extreme temperature events are of key importance for improving temperature related mortality forecasts. Finally, we highlight the implication of these results in a warming climate. In the absence of meaningful adaptation measures or considerable advances in numerical weather prediction capabilities, temperature related mortality forecasts will be associated with larger errors owing to increased climatological temperatures. We emphasise that continued work on understanding the predictability of temperature extremes and temperature related mortality is vital for the further development of heat-warning systems.

How to cite: Holmberg, E., Quijal-Zamorano, M., Messori, G., and Ballester, J.: The predictability of temperature related mortality in the summers of 2022 and 2023, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-408, https://doi.org/10.5194/ems2024-408, 2024.

The properties and distributions of precipitation are often determined by specific synoptic patterns. Hence, the objective identification of corresponding impact patterns is an important field of research for improving rain forecasting. However, the identification of the weather patterns producing intense rainfall is much more challenging. Since they are violent and local, impact patterns tend to be meso- or smaller-scale systems and are often incompletely presented or only presented in limited regions. In this paper, a deep learning network with a feature cross-fusion module, FConvNeXt, was proposed to address this difficulty and showed great potential. Four major patterns corresponding to intense rainfall in the Beijing–Tianjing–Hebei Region were studied. Statistical testing showed that FConvNeXt performed better than ConvNeXt and ResNet and that the model could identify the weak synoptic forcing type, the subtropical high-pressure type, and the low-vortex pattern with high accuracy. Furthermore, a strictly independent 2021 dataset was tested, and FConvNeXt maintained an equal if not even slightly better performance in spite of a decrease in the subtropical high-pressure type. Meanwhile, the study showed that the accuracy in identifying the upper-level trough type is the lowest for the three deep learning methods, which maybe because the northeast vortex was intercepted in the limited region, making  it difficult to distinguish from the shallow upper-level trough type. This study is useful for improving the fine objective of forecasting intense rainfall.

In summary, in contrast to previous objective classifications on large-scale weather systems in large regions, this study explored the objective classification of meso- and small-scale weather patterns that correspond to heavy rainfall and flash flooding within a limited region. Advanced deep learning models were employed that showed significant potential for this application. Furthermore, a new cross-fusion feature extraction module was proposed that improved the accuracy of the LVT classification within a limited region. Moreover, the study introduced a pre-training model to improve the training speed, which improved the accuracy and significantly shortened the training time.

How to cite: Zhong, Q. and Jing, L.: Identify Patterns of Flash Heavy Rainfall in Limited Area by FConvNeXt, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-538, https://doi.org/10.5194/ems2024-538, 2024.

A cluster of severe extratropical cyclones (Dudley, Eunice and Franklin) hit North-Western Europe within one week in February 2022, and caused widespread damage and fatalities by strong wind gusts and high accumulated precipitation amounts. These cyclones developed over the North-Atlantic within a baroclinic environment with strong jet streams accompanied by atmospheric rivers. Extratropical cyclone clustering is counterintuitive because individiual cyclones reduce large-scale temperature gradients and baroclinicity that are essential for their growth. We hypothesise that diabatic heating through latent heat release enhances the baroclinic environment favourable for secondary cyclogenesis. To quantify the influence of latent heat release on this baroclinic environment, we performed idealised model experiments with the Open Integrated Forecast System (OpenIFS) from the European Center for Medium Range Weather Forecastst (ECMWF). The latent heat of vaporisation constant was enhanced and reduced by 50 percent respectively.

The control experiment captured the location, speed and direction of the jet stream, and the path and intensities of the individual cyclones well. The model results show that reduced latent heating weakens the jet stream strength, while enhanced latent heating strengthens the jet stream strength. The baroclinic environment responds similarly — i.e., the meridional temperature gradient decreases with reduced latent heating and increases with enhanced latent heating. We plan to apply the isentropic slope diagnostic for baroclinicity to quantify the diabatic contributions of latent heating to the baroclinic environment. We also plan to discuss the effects of latent heating on the individual cyclones. With this case-study we explore diabatic heating as a pathway for extratropical cyclone clustering.

How to cite: Batelaan, T. J., Weijenborg, C., and Steeneveld, G.-J.: The Role of Latent Heating in a North-Atlantic Baroclinic Environment Conducive to Extratropical Cyclone Clustering, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-634, https://doi.org/10.5194/ems2024-634, 2024.

How to cite: Chan, K. and Mellado, J. P.: How do the small-scale details of the radiative transfer shape the development of stratocumulus? , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-132, https://doi.org/10.5194/ems2024-132, 2024.

Turbulence anisotropy has recently gained attention for its role in the study of surface layer turbulence. In particular the degree of anisotropy, quantified through the anisotropy invariant y_B, has been successfully introduced as an additional non-dimensional parameter into the Monin-Obukhov Similarity Theory (MOST), and tested on the flux-variance and flux-gradient surface scaling relations. The novel extended MOST relations were shown to explain the observed scatter in the MOST scaling relations both over flat and highly complex terrain, thus allowing MOST to be extended outside of its restrictive original assumptions. The challenge, however, still remains in how to predict y_B for a range of realistic conditions, which would allow to implement the novel scaling relations in numerical models’ surface parametrizations.

In this study we use data from both flat (AHATS) and complex terrain (Perdigao measurement campaign), to understand the drivers of turbulence anisotropy. We use interpretable machine learning techniques considering a wide range of macro and micro meteorological variables, surface heterogeneity, and topographic measures to build a predictive model using a tree-based regression algorithm (i.e. random forest and boosting algorithms). Interpretability techniques, such as variable importance measures, partial dependence plots, and Shapley analysis then allow us to select the variables that influence turbulence anisotropy the most and assess their relation with each other and with our target variable. This approach will ultimately lead to an understanding of the processes behind the emergence of different states of turbulence and its anisotropy, paving the way for robust surface parametrizations in numerical weather models.

How to cite: Mosso, S., Lapo, K., and Stiperski, I.: Interpretable machine learning reveals the drivers of turbulence anisotropy over complex terrain., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-215, https://doi.org/10.5194/ems2024-215, 2024.

  The most widely accepted concept of energy balance in the volume of the urban canopy layer is expressed by the following equation,

Q* + Q_F = Q_H + Q_E + ΔQ_S + ΔQ_A

where Q* is the net all-wave radiation, Q_F  the anthropogenic heat flux, Q_H the turbulent sensible heat flux, Q_E the latent heat flux, ΔQ_S the net storage heat flux and ΔQ_A the net advective heat flux.

Through observations and simulations targeting a residential area in Tokyo, this study proposes revisions to the above conventional concept for energy balance in the following two respects.

1. Q_F  should be interpreted not as the anthropogenic exhaust heat released to the urban atmosphere but as energetic bulk input to the urban canopy where Q_F  is equivalent to heat resulting from fuels combustion and electricity consumption with usually negligible metabolic heat there.

2. Instead, actual anthropogenic exhaust heat should be treated as components of the turbulent heat fluxes (Q_H and Q_E) using the following equation,

Q_H = Q_Hb + Q_Ht + Q_Hs

where Q_Hb is the net building anthropogenic heat consisting of waste heat from HAC (heating and air conditioning) systems and exchanged heat between indoors and outdoors through ventilation, Q_Ht the anthropogenic heat from traffic and Q_Hs the turbulent sensible heat flux (convection) from the urban surfaces. The same concept holds true for the latent heat.

   The above revisions are applied in this study to the full energy flow analysis for the residential urban canopy in Tokyo using the eddy covariance CO₂ flux and O₂ & CO₂ concentrations measurements, together with fine resolution (time and space) inventory data of electricity use and car traffic. Through the analysis, we estimate Q_F  by source (electricity, liquid and gas fuel) and each component of the turbulent heat fluxes (Q_Hb, Q_Ht and Q_Hs). Finally, those observation-based estimates are compared with the simulations with a focus on Q_Hb. The authors’ multi-layer urban canopy model coupled with the building energy model is used for the simulations. Resultantly, observation-based components of urban energy balance show good agreement with those from simulations including Q_Hb, suggesting the validity of the authors’ revisions to the conventional urban surface energy balance concept.

How to cite: Kikegawa, Y., Sugawara, H., Kobayashi, T., and Maruyama, N.: How should the anthropogenic heat be treated in the urban surface energy balance: revisit of the definition of QF, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-245, https://doi.org/10.5194/ems2024-245, 2024.

The concave mountainous area of Pearl River Delta is a summer rainfall hotspot along the South China coast due to the presence of warm-moist monsoon flow and complex orography. This study evaluated the performance of a convection-permitting Weather Research and Forecasting (WRF) model in forecasting nocturnal rainfall in this area, focusing on days with low level southwesterly winds during the summers of 2013–2015. Results showed that the nocturnal rainfall exhibited two centers, one located along the large-scale northern mountains and the other along the small-scale Huadu Hill. WRF demonstrated superior performance in predicting rainfall over the northern mountainous region. In contrast, WRF significantly underestimated nocturnal rainfall both near local Huadu Hill and in the foothill area of northern mountains, which were strongly influenced by local forcings. Using high-resolution analyses from Variational Doppler Radar Analysis System (VDRAS), which assimilated both Doppler radar and Automatic Weather Stations observations, we firstly investigated the mesoscale mechanism governing the convection initiation (CI) of a typical localized nocturnal convection. Results showed that the enhanced prevailing low-level southerly winds, combined with local circulation induced by the urban heat island effect and orographic forcings, led to the formation of low-level convergence and strong updrafts before CI. Subsequently, we identified the sources of forecast errors in triggering CI. Results revealed that WRF severely underestimated thermal contrast between the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration and the concave mountains, leading to the absence of the northeastern/northern inflows toward the cities. Consequently, low level convergence and updrafts near the CI position were too weak to lift air parcels above the severely overestimated level of free convection, thereby failing to trigger the convection.VDRAS-based sensitivity experiments, with a specific focus on assimilating surface temperature, validated the crucial role of urban-mountain thermal contrast on local winds that triggered the nocturnal convection.

This study underscores the significance of urban-mountain thermal contrast and local circulations in determining nighttime precipitation formation and prediction, particularly in geographically complex regions characterized by concave mountains and urban agglomerations. The findings highlight the need for improved representation of these local forcing mechanisms in numerical weather prediction models to enhance their accuracy in forecasting nocturnal rainfall events.

How to cite: Rao, X., Zhu, K., Zhao, K., and Chen, X.: Analysis of Triggering Mechanisms for Localized Nocturnal Convection Over a Complex Mountainous Region in South China and Sources of Forecast Errors in the Convection-Permitting Model, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-36, https://doi.org/10.5194/ems2024-36, 2024.

This article presents the first systematic climatology of severe thunderstorms (ST) in the Balearic Islands (Spain) between 2010 and 2023. Thanks to severe weather reports and observations from 111 weather stations, an extensive database was constructed involving 43 severe thunderstorms affecting the islands during 41 severe storm days. A thunderstorm was considered as severe when producing at least one of the following  phenomena: hail with diameter ≥ 2 cm, straight line winds ≥ 90 km/h or a tornado over land. Annual, monthly, seasonal, fortnight, spatial and hourly distributions were analyzed. Autumn months account for 48.8% of all ST, with the highest frequency seen in October. Distribution by fortnights shows a maximum of 7 ST in the second half of August. Convective straight line winds were the most common severe phenomena in ST affecting the Balearic Islands and occurred in 41.9% of all ST. Hourly distribution shows a maximum in the morning hours, between 10 and 12 AM. Spatial distribution shows a maximum incidence in the biggest island (Mallorca) with a mean of 2.6 ST/year. The South of Mallorca followed by the central part of the island are ST hotspots within the archipelago. For every severe thunderstorm day, the mean SST and anomaly of the Balearic Sea were identified using daily reanalysis from 12 UTC on ERA5. The vast majority (84%) of ST that make landfall in the islands are maritime. Peak ST activity occurred with SST in the range of 25-26ºC. Extremely severe thunderstorms showed a maximum with SST between 27 and 28ºC. 

How to cite: Wingen, D. and Jansà, A.: Severe thunderstorm climatology in the Balearic Islands (2010-2023), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-85, https://doi.org/10.5194/ems2024-85, 2024.

Atmospheric deserts are air masses originating in the hot, dry boundary layers in semi-arid or desert source regions, which are then advected across target regions with cooler, moister boundary layers. The novel approach in this study tracks the air masses and the development of their properties directly along their path from the source to the target region. This is done using trajectories calculated with the Lagrangian analysis tool (LAGRANTO), which are initiated at a very high spatio-temporal resolution in the source region.

Typical pathways can be identified using k-means-clustering. Based on these clusters, we find that most of the atmospheric desert air is modified along the path due to diabatic processes. Radiative cooling and especially latent heating and cooling in consequence to cloud formation and  re-evaporation of falling precipitation are the main causes for the changes in the thermodynamic properties of the atmospheric desert air.

Due to their higher potential temperature, atmospheric deserts can form a lid over the local boundary layer in the target region. The lid can prevent the local boundary layer from growing, which leads to heat build-up under clear sky conditions. If the atmospheric desert prevails long enough, this can lead to a heat wave. Similarly, the warm air aloft suppresses the formation of thunderstorms in its centre, while at the same time the convective available  potential energy can grow. However, thunderstorms do erupt violently closer to the edges of the atmospheric desert, where the lid can be expected to be higher and weaker and where trigger mechanisms initiate rising motion.

In this study we use case studies from March and June 2022, and June 2021 to analyse the processes modifying the air mass enroute, and its influence on heat wave and thunderstorm formation. In June 2022, Europe experienced extreme heat, in June 2021 tornadoes were observed in Bohemia, and March 2022 can serve as a case from a different season. These diverse case studies are therefore a good starting point for analysing the above questions.

How to cite: Fix, F., Mayr, G., Stucke, I., and Zeileis, A.: Hot or Boom? The Influence of Atmospheric Deserts on Heat Wave and Thunderstorm Formation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-216, https://doi.org/10.5194/ems2024-216, 2024.

Hailstorms and other convective events present significant societal and economic risks, with challenges in assessing these risks due to inconsistent data. The main goal of our research is to help mitigate the impacts of hailstorms by generating high-resolution risk maps. While high-resolution simulations with models like the Weather Research and Forecast (WRF) are key for generating risk maps, these models are computationally expensive. To address this challenge, we have developed a machine learning model, the Convective Days Detector (CDD), which uses ERA5 reanalysis data to identify the days when the probability of deep convection is high. The CDD serves as a starting point for hail event simulations, reducing the number of days to simulate to essential ones, and thereby reducing computational costs. 

We will provide a comprehensive analysis of the CDD, discussing its performance in detecting convective events and the process we followed to refine its capabilities. Our analysis began with a detailed examination of essential instability variables Convective Inhibition (CIN) and Convective Available Potential Energy (CAPE) from the ERA5. This analysis involves comparing different packages for calculating CAPE and CIN with ERA5 data. Despite finding differences, particularly in high CAPE values, we concluded that these discrepancies are not significant for a machine learning model. Therefore, we use ERA5 CAPE due to its importance in detecting convective events and opted not to use CIN in our model due to some inconsistencies we found. Following this, we performed a feature analysis to further refine our model, reducing the number of variables used to eight essential ones for detecting convection. We validated the model to ensure its functionality and introduced a spatial error margin to assess spatial inaccuracies in convective day detection. This validation process involved verifying the performance of the model against known convective events and evaluating its ability to accurately identify convective days. 

Performing high-resolution WRF simulations on days with high deep convection likelihood, we aim to provide a more detailed understanding of the dynamics and impacts of extreme weather events. We will discuss how we plan to use WRF to simulate the physical processes of these events and how these simulations can contribute to the generation of more accurate risk maps. By combining insights from meteorological models, machine learning algorithms, and risk mapping, this research aims to provide a comprehensive framework for understanding and predicting and minimise the socio-economic impact of hailstorms across diverse sectors.

How to cite: Guerrero-Calzas, I., Sanchez-Marroquin, A., Barcons, J., Tuinenburg, O., Cortés Fité, A., and Miró Cubells, J. R.: Mitigating hailstorm impacts: an integrated approach using Machine Learning and physical simulations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-350, https://doi.org/10.5194/ems2024-350, 2024.

Among severe weather events related to deep moist convection, hail represents one of the most hazardous perils for terrestrial ecosystems and human-related activities such as agriculture and building and insurance sectors. Furthermore, the severity and frequency of high-impact hailstorms is increasing throughout Europe, with the highest potential to worsen expected over northern Italy. However, multiple limitations hinder a comprehensive understanding of where and when severe hail can occur. Indeed, the scarcity and incompleteness of information provided by the available observations, and the approximate representations possible with numerical simulations limit a thorough assessment of hail occurrence. These challenges call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modeling.

In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting SPHERA reanalysis predictors describing hail-favorable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data selection. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter built to sub-sample the OTs, namely: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing level height. A hail frequency estimate over the extended summer season (April-October) in south-central Europe is presented for a test period of 5 years (2016-2020). OT-derived hail frequency peaks at around 15 UTC in June-July over Italian pre-Alpine regions and the northern Adriatic sea. The hail proxy statistically matches with ∼63% of confirmed ESWD reports, which is roughly 23% more than the previous estimate over Europe obtained by coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstones diameters ≥ 3 cm). Further, signatures for small-hail missed occurrences are identified, which are characterized by lower instability and organization, and warmer cloud-top temperatures.

How to cite: Giordani, A., Kunz, M., Bedka, K. M., Punge, H. J., Paccagnella, T., Pavan, V., Cerenzia, I. M. L., and Di Sabatino, S.: Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-544, https://doi.org/10.5194/ems2024-544, 2024.

Lightning represents only 5-10% of fire causes in Europe and 1-2% in Portugal and correctly predicting its occurrence can help to mitigate large forest fires. In 2016, the European Centre for Medium-Range Weather Forecasts (ECMWF) introduced a parametrization for lightning forecast. Therefore, aiming to assess the feasibility of using ECMWF forecasts in the context of natural wildfires, this study analyses the ECMWF data for mainland Portugal over 4 fire seasons (2019 - 2022). The dataset used is composed by the total average lightning density in 3 hours from the ECMWF and the observation data from the detection network from IPMA and grouped for the same times. The data was also aggregated into horizontal resolution grids of 1⁰ and 0.5⁰. To carry out the statistical analysis, two contingency tables were constructed, and some statistical indices were calculated (e.g., BIAS, Success rate, False alarm rate, Threat score, Equitable threat score, True skill score). The results reveal a Bias score bigger than 1, indicating that model tends to overestimate the lightning occurrence. Moreover, the success rate was about 57.7% (1⁰) and 49% (0.5⁰). The temporal analysis indicated a time lag between both data, with the model starting and ending the prediction earlier than observed. Furthermore, two meteorological patterns were found producing lightning during the period studied: the development of a thermal low overlapping with an Upper Level Low and the passage of large-scale systems. These results show that the ECMWF lightning forecast can be used as an additional tool to prevent natural fires, with the model being able to indicate the days and locations of lightning occurrence. The research was funded by the European Union through the CILIFO project (0753-CILIFO-5-E) and also by national funds through FCT Foundation for Science and Technology, I.P. under the PyroC.pt project (PCIF/MPG/0175/2019).

How to cite: Campos, C., T. Couto, F., L.M. Santos, F., Rio, J., Ferreira, T., and Salgado, R.: ECMWF lightning forecasts evaluation during fire seasons in Portugal, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-593, https://doi.org/10.5194/ems2024-593, 2024.

Tornadoes are the meteorological phenomenon capable of producing the most intense surface winds in the Earth, sometimes exceeding 100 m s^-1. Tornado wind speed can rarely be measured in-situ using anemometers and radar. Nevertheless, it can be estimated through an analysis of the observed damage, especially when performing in-situ damage surveys (Rodríguez et al., 2020). The Fujita (F) scale (Fujita, 1971), which relates damage on buildings and forest with wind speed, was proposed with the aim of assessing tornado intensity. In the early 2000s was revised by the Texas Tech University proposing the Enhanced Fujita (EF) scale (WSEC, 2006). Both F and EF scales are broadly used, although it is hampering to apply them out of the USA because most damage indicators are related to typical building structures of that country, which are significantly different from those which are common in other regions.

Recently, it has been proposed the International Fujita (IF) scale, which has been developed by several contributors from European universities and meteorological services coordinated by the European Severe Storm Laboratory (ESSL, 2023). IF scale considers a large variety of constructive structures and its sturdiness. This allows carrying out a detailed wind speed estimation based on damage surveys.

In this work we revisit the 122 tornadoes included in the Catalonia tornado database 2000-2023 (NE Iberian Peninsula) with the aim of classifying events according to the new IF scale. We analyse and discuss spatial and temporally the results. Moreover, we compare them with previous classifications performed using F and EF scales, which showed that 92% of tornadoes reported in the region were weak (EF0 or EF1), whereas 8% were significant (EF2 or stronger) (Rodríguez et al., 2021). This study is partly supported by project PID2021-124253OB-I00.

References

ESSL, 2023. The International Fujita (IF) Scale for tornado and wind damage assessments. European Severe Storm Laboratory, Wessling, Germany. https://www.essl.org/cms/wp-content/uploads/IF-scale_v1.0d.pdf

Fujita T.T., 1971. Proposed characterization of tornadoes and hurricanes by area and intensity. SMRP Research Paper, 91: 48.

Rodríguez O., Bech J., Soriano J.D., Gutiérrez D., Castán S., 2020. A methodology to conduct wind damage field surveys for high-impact weather events of convective origin. Nat. Hazards Earth Syst. Sci., 20 (5): 1513-1531. https://doi.org/10.5194/nhess-20-1513-2020

Rodríguez O., Bech J., Arús J., Castán S., Figuerola F., Rigo T., 2021. An overview of tornado and waterspout events in Catalonia (2000–2019). Atmos. Res., 250: 105415, https://doi.org/10.1016/j.atmosres.2020.105415

WSEC, 2006. A Recommendation for an Enhanced Fujita Scale (EF-scale). Wind Science and Engineering Center (Texas Tech University), Lubbock, Texas, USA. https://www.depts.ttu.edu/nwi/Pubs/EnhancedFujitaScale/EFScale.pdf

How to cite: Rodríguez, O., Bech, J., and Castán, S.: Revisiting a 24-year database of tornado damage paths using the new International Fujita (IF) scale, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-599, https://doi.org/10.5194/ems2024-599, 2024.

Understanding and predicting tropical cyclone (TC) activities are important for mitigating TC risks to human society. However, general circulation models (GCM) are integrated at moderate horizontal resolutions (~50 km) in climate projections; TC characteristics in these runs tend to be unrealistic and very sensitive to how convection is parameterized. The so-called multiscale modeling framework provides a way to improve GCMs by inserting a cloud resolving module- but only within each model grid column to “super-parameterize” convective-related processes.

Here we compared the TC activities in the super-parameterized Community Atmosphere Model (SPCAM) v5 with those in the convectively parameterized CAM (CPCAM, v5; Zhang and McFarlane deep convection and Park and Bretherton shallow convection schemes), in aquaplanet, perpetual summer experiments. It was found that SPCAM had a TC frequency 4-5 times higher than that produced by CPCAM. In SPCAM the genesis potential index and 500-hPa pressure velocity were also significantly stronger, while the SPCAM ventilation index was found to be weaker off the equator than that in CPCAM, indicating an environment more conducive for TC genesis. More TC “seeds” were also found in SPCAM compared to CPCAM. For individual TCs, there were stronger maximum 10-m wind speed and lower minimum sea level pressure, while the wind-pressure relationship was also more realistically captured, in SPCAM compared to CPCAM. Further inspection of the GCMs’ general circulation revealed an Inter-topical Convergence Zone (ITCZ) farther (closer) to the equator, with more (less) frequent ITCZ breaking in SPCAM (CPCAM). SPCAM also exhibited enhanced equatorial Rossby and mixed Rossby-gravity wave activities, as well as tropical depression-type systems, in its model environment. Implications were drawn for applying a multiscale modeling framework in GCMs to reduce uncertainties in climate and extreme event modeling.

How to cite: Chow, T. N., Tam, C. Y., and Chung, E. T. S.: Large-scale Influences on Tropical Cyclone Activities in a Superparameterized GCM Aquaplanet Experiment, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-694, https://doi.org/10.5194/ems2024-694, 2024.

Compound precipitation and wind extremes (CPWEs) are multivariate compound climate events that have received increasing attention recently. Such extremes can cause damage to buildings and infrastructure that results in extensive socio-economic losses. The main objective of this work is to assess the recurrence and intensity of CPWEs in the eastern part of the Baltic Sea region from 1950 to 2022.

This study investigated the region between 53.5° to 59.5° N and from 20° to 28.5° E. Daily precipitation and hourly maximum wind speed data with a grid size of 0.25 x 0.25° were obtained from the ERA-5 reanalysis database to identify CPWEs. Then, for the entire study period and each grid cell within the study area, the 98th percentile of daily maximum wind speed and precipitation were calculated. A CPWE was distinguished when precipitation and maximum wind speed values exceeded the 98th percentile values of the corresponding variable on the same day for at least one point in the study area.

Two different approaches were employed to evaluate the intensity of CPWEs. Firstly, terciles of maximum wind and precipitation were calculated, and the values of each variable were allocated into three categories, of which nine intensity classes of CPWEs were formed. While applying the other method, the number of CPWEs, when one or both of these variables exceeded the 99th percentile values in at least one grid cell of the study region, was computed.

A total of 797 CPWEs have been distinguished from 1950 to 2022. During the study period, the number of these events significantly increased (when p < 0.05). The highest number of CPWEs occurred in October. During the cold season (from October to March), most of these compound events were obtained in western Lithuania, while during the warm season (from April until September) – in the northeastern and south-eastern parts of the study region.

Evaluation of the intensity of CPWEs using the first approach revealed that all events characterized by extremely high amounts of precipitation (> 80 mm per day) occurred in June and September. Meanwhile, 88% of CPWEs, during which the wind speed was higher than 28 m/s, were obtained from October until March. The most severe CPWE occurred on August 7, 1987, in the northeastern part of the study region. However, using the other approach, the most intense CPWE was identified on October 28, 1998, when the values of both maximum wind speed and precipitation exceeded the 99th percentile values of the corresponding variable in 37.8% of the grid cells of the study area. The number of CPWEs when the values of both variables exceeded the 99th percentile slightly increased over the study period.

How to cite: Klimavičius, L.: Compound precipitation and wind extremes in the eastern part of the Baltic Sea region, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-716, https://doi.org/10.5194/ems2024-716, 2024.

Backward trajectories have been used to assess the relative importance of the moisture source regions contributing to the heavy precipitation during the July 2021 flooding event. Mohr et al. (2023) suggest that the moisture fueling the extreme event was mostly advected from the North Sea and Baltic Sea, which displayed unusually high sea surface temperatures. In turn, Weijenborg et al. (2022) suggest that the water mostly originated from land surfaces in the proximity of the event. The water, which then would have had to be evaporated over adjacent regions, presumably was available due to high amounts of rainfall leading up to the event. They find smaller contributions from the North and Baltic Seas.

To quantify the input from these source regions towards the heavy precipitation, we perform a sensitivity study. Here, we carry out free ensemble forecasts of the event using the ICON model starting from DWD analysis fields. We then vary the initial conditions with respect to soil moisture. Our sensitivity study can also be seen as a storyline approach, which allows us to assess the role of high soil moisture as a forcing of the heavy precipitation. This is directly related to our work in the collaborative research center DETECT, where we hypothesize that regional changes in land and water use alter the onset and evolution of extremes. This study allows us to test how well we are able to detect changes in the characteristics of the July 2021 flood event based on variations in the terrestrial water budgets.

How to cite: Fohrmann, T. and Friederichs, P.: Impact of soil moisture on the heavy precipitation during the July 2021 flood, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1054, https://doi.org/10.5194/ems2024-1054, 2024.

Design precipitation intensity is an essential variable used in water management practice. In particular, it is essential for the hydrological modeling of proposed measures in ungauged catchments. However, point design precipitation totals cannot be used in larger catchments as they do not include information on the areal variability of precipitation. Therefore, in practice, point design precipitation totals are reduced to catchment areas using empirically derived Area Reduction Factors that are subject to a number of uncertainties. To reduce these uncertainties, we present here an evaluation of areal design precipitation derived from radar data using the Czech Republic as an example.

The input dataset is radar reflectivity data at an altitude of 2 km (pseudo-CAPPI 2km) for 20 years between 2002 and 2021 with a temporal resolution of 10 minutes and a spatial resolution of 1 km. These data are then adjusted with daily precipitation totals from stations. From the adjusted precipitation intensities, precipitation totals are determined for durations from 30 minutes to 3 days. From these rainfall totals, their areal averages are calculated for individual catchments at four hierarchical levels. From these, the L-moment method is used to derive Generalized Extreme Value (GEV) distribution parameters, which are used to determine design areal precipitation totals in the considered catchments for all considered durations.

Naturally, as the catchment area increases, the areal design precipitation decreases. The average  1-hour design precipitation total with a return period of 20 years is 36 mm for the smallest catchments, whereas the design precipitation is approximately half of that for a catchment of 1000 km². However, for longer periods of rainfall accumulation, the decrease is not as pronounced. Therefore, on larger catchments, larger ratios of the design totals are achieved between longer and shorter accumulation times. In terms of the spatial distribution of design areal precipitation totals, the design 1-hour totals are very randomly distributed, with an obvious dependence on the catchment size. On the contrary, in the case of the design 24-hour totals, the influence of georelief is a major factor, with the highest totals being strongly concentrated in the mountains and their foothills.

We have shown that the adjusted radar data are suitable for estimating design area precipitation over the catchment, although the longer return periods are particularly subject to the uncertainty caused by the short data series. The demonstrated influence of georelief on longer accumulation times then points to the inappropriateness of the approach of deriving design areal precipitation using reduction coefficients empirically derived from point values for large areas. Radar data, allowing direct calculation of design areal precipitation totals for a specific catchment, seem to be more appropriate.

How to cite: Hulec, F., Kašpar, M., and Müller, M.: Design areal precipitation in Czech catchments – radar data usage instead of station data reduction, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1027, https://doi.org/10.5194/ems2024-1027, 2024.

The National Institute of Atmospheric Sciences (NIMS) employs its KingAir 350H research aircraft for various missions, including the "Severe Weather" (SW) mission. This mission aims to enhance understanding of severe weather events like heavy rain, snowfall, and typhoons through advanced observations, ultimately improving numerical model accuracy. A key instrument aboard the aircraft is the Stepped Frequency Microwave Radiometer (SFMR). Mounted underneath, this C-band radiometer measures ocean brightness temperature to calculate sea surface wind speed and rain rate. Our study compared wind speeds derived from the SFMR with those obtained from dropsondes deployed along the aircraft's flight path. We also investigated the sensitivity of SFMR wind speed estimates to sea surface temperature and salinity, both serving as initial input data. Between 2021 and 2023, 118 SW-mission flights around the Korean peninsula. SFMR data revealed an average wind speed of 8.7 m/s and a maximum of 22.1 m/s. Sea surface temperature and salinity data from buoys and research stations were interpolated to 0.1˚ intervals for sensitivity analysis. Calibration coefficients derived from a dedicated calibration flight on October 26, 2022, were applied to re-calculate wind speeds, which were then compared with pre-calibration estimates and wind speeds obtained from dropsondes at 30m, 150m, and 500m above the sea surface. Notably, the strongest correlation was observed between SFMR-derived wind speeds and those at 500m. This study demonstrates the potential of NIMS aircraft SFMR data for accurate sea surface wind speed measurements, particularly when employing calibration and considering the influence of lower-level wind patterns. Further research can refine these techniques and enhance our understanding of air-sea interaction.

How to cite: Kang, D., Kim, M., Han, S., Goo, T., Jung, S., Lee, G., Shin, J., and Kang, M.: Sea surface wind measurement enhanced by stepped frequency microwave radiometer: A comprehensive approach to validation and sensitivity analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-375, https://doi.org/10.5194/ems2024-375, 2024.

Mineral dust is one of the biggest contributors of particulate matter in the atmosphere, and its transport as well as atmospheric implications has been a topic of interest for the last few decades. Still, mineral dust dynamics are often not appropriately accommodated in model simulations, which require in-situ datasets for more accurate representation of dust events. In our study, the uncrewed aircraft system (UAS) of type MASC-3 was employed for measurements of meteorological parameters (potential temperature, mixing ratio and the three-dimensional wind vector at high temporal resolution), turbulent quantities, aerosol particles and space charge, during an intense dust event over Cyprus in spring, 2022. Vertical profiles up to 3000 m above sea level (a.s.l) revealed how the dust layer had peaks at 1800 and 2500 m, as well as affected the extent of the atmospheric boundary layer. The measurements from the UAS were in good agreement with remote sensing instrumentation in the vicinity of the flights, with the observations being further supported by satellite imagery and back tracking model simulations. Vertical profiles of charge were also co-related to approximated modeled one-dimensional profiles of the expected charge for the given dust concentrations. The results showed several layers of charge throughout the dust cloud’s vertical extent, most notably at its upper and lower boundaries, as anticipated from theoretical calculations. This is the first time charge measurements were made possible using a UAS, and in combination with data for dust concentrations and meteorological quantities, new insights can be discovered regarding mineral dust charge, characteristics and its interaction with the atmosphere.

How to cite: Savvakis, V., Schön, M., Bramati, M., Bange, J., and Platis, A.: In-situ measurements of Saharan dust concentrations and charge over Cyprus with an uncrewed aircraft system, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-511, https://doi.org/10.5194/ems2024-511, 2024.

Evapotranspiration (ET) is a key process in the hydrological cycle that can help mitigate urban heat. ET depends on the surface cover, as the surface affects the partitioning of precipitation between runoff and evapotranspiration. In urban neighborhoods, this surface cover is highly heterogeneous. The resulting neighborhood-scale ET is observed with eddy-covariance systems. However, these observations represent the signal from wind- and stability-dependent footprints resulting in a continuously changing surface cover composition. This continuous change prevents quantitative analysis of the separate types. Here, we disentangle this neighborhood-scale ET at two urban sites in Berlin attributing the ET dynamics to the four major surface cover types in the footprint: impervious surfaces, low vegetation, high vegetation, and open water. Starting from the surface, we reconstruct ET based on patch-scale observations and conceptual models. Alternatively, we start with the eddy-covariance observations and attribute ET to the surface cover types solving a system of equations for four eddy-covariance systems with different footprints. Although starting at the surface yields more robust results, both approaches indicate that vegetation is responsible for more ET than proportional to its surface fraction, and evaporation from impervious surfaces although less cannot be neglected. The behavior of each surface cover type is separated allowing to study the response to rainfall for each type separately. Impervious surfaces exhibit a strong ET peak directly after the rainfall event, while open water is insensitive to the rainfall. High vegetation starts with high ET after rainfall and limits ET within the first days, but low vegetation does not start limiting ET until the end of the warm season likely reaching the soil moisture limit. Additionally, we confirm the intuitive relation between ET and the surface cover fractions based on a wide range of surface compositions.

How to cite: Jongen, H., Vulova, S., Meier, F., Steeneveld, G.-J., Jansen, F., Tetzlaff, D., Kleinschmit, B., Haacke, N., and Teuling, R.: Attributing urban evapotranspiration from eddy-covariance to surface cover: bottom-up versus top-down, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-97, https://doi.org/10.5194/ems2024-97, 2024.

The aim of this paper is to determine the impact of the city on the intensity and duration of heat waves. The study was carried out in Vilnius city, Republic of Lithuania. Vilnius is the capital and largest city of Lithuania, with a population of 590 000 as of 2023. The area is 401 km² and the density is 1450/km². The central part of the city is located in the wide and deep valleys of the Neris and Vilnia rivers, while the other districts of the city are located on the surrounding hills and their slopes. The lowest point of the city is 97 m above sea level and the highest is 234 m.

An urban heat island (UHI) can amplify and prolong heat waves. This is important for the design and assessment of the energy performance of buildings in urban areas and the impact of heat waves on human health. The role of UHI will be even greater in the future due to climate change and possible global warming, as temperatures are likely to rise and the UHI will exacerbate them.

In this study, hourly air temperature data, as well as daily average, maximum, and minimum air temperature data were collected from Vilnius University automatic meteorological station (VU MS) located in the city centre, and automatic stations at Vilnius Airport (VA) in the suburbs during the summer seasons of 2022–2023.

In Lithuania, a heatwave is a natural meteorological phenomenon where the daily maximum air temperature reaches 30 °C or more for 3 consecutive days (or more). In total, 30% of the days in the summer of 2022 had a daily maximum air temperature above 30 °C in VU MS, while only 16% of the days in the suburban VA meteorological station reached this threshold. In the summer of 2023, 20 % and 7 % of such days were found, respectively. According to the VU MS data, 7 heat waves with a total duration of 32 days have been recorded for 2022–2023, while only 3 heat waves with a total duration of 11 days have been recorded in the suburban (VA) area. In the central part of the city (VU MS), the average daily maximum air temperature during heatwaves was 3.2 °C higher than in the suburban VA. It was also found that tropical nights (daily minimum air temperature of at least 20 °C) were more frequent in the city compared to the suburbs, and the amplitude of the daily air temperature increased during the heat waves.

How to cite: Bukantis, A. and Klimavičius, L.: Urban effects on heatwave intensity and duration: a case study of the Vilnius city, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-130, https://doi.org/10.5194/ems2024-130, 2024.

Nowadays, atmospheric pollution is one of the most relevant environmental issues. Some pollutants such as fine particulate matter with a diameter of 10 μm or less (PM₁₀) have a considerable impact on human health. In Catalonia (NE Spain), Saharan Dust Intrusions are a major source of PM₁₀. In recent decades, a positive trend of these intrusion episodes has been detected in the NW Mediterranean basin. Moreover, precipitation plays a role in pollutant scavenging processes.

In our work, we make an analysis of the changes in PM₁₀ concentrations with the precipitation episodes. Moreover, we put a special emphasis in the precipitation episodes which happen simultaneously with a Saharan Dust Intrusion. In consequence, we have analysed PM₁₀ concentration data from the Catalan Network for Pollution Control and Prevention and precipitation data from Automatic Weather Stations Network of the Meteorological Service of Catalonia. We use data from four measurement points in Catalonia (Montsec Observatory, Fabra Observatory, Vic and Sort) which are the unique points with precipitation and PM₁₀ measurement instruments at the same location. Dataset contains 4-year data from 2019 to 2022. Specifically, we evaluate how daily mean PM₁₀ concentration values for all the days in the dataset change in comparison to the values of the same variable for the previous day. Moreover, we do a separate analysis for days with observed precipitation (wet days) and days without precipitation (dry days). Also, we perform the analysis for days with Saharan Dust Intrusion and days without Saharan Dust Intrusion. Furthermore, we filter daily PM₁₀ concentration changes for different absolute values of this daily variation to see the differences between great and small changes of daily PM₁₀ concentration. To our knowledge, this is the first study of these characteristics in this region of study.

In general, we observe a decrease of daily PM₁₀ mean concentration levels with precipitation in approximately 60% of the days. This percentage increases to 80% for daily changes of PM₁₀ concentration higher than 10 µg m^-3. In wet days with Saharan Dust Intrusion, daily PM₁₀ mean concentration decreases only in 50% of the cases independently of the absolute value of PM₁₀ concentration variation. However, in wet days without Saharan Dust Intrusion, daily PM₁₀ mean concentration decreases approximately in 60% of the cases. This percentage grows up to 90% if we only consider changes of PM₁₀ concentration higher than 10 µg m^-3. In consequence, Saharan Dust Intrusions clearly interfere with the usual pollutant precipitation scavenging processes. In addition, we find that scavenging processes are more effective above a certain PM₁₀ concentration variation threshold. This study was performed in the framework of the project "Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE22-IPC-ES-LIFE PYRENEES4CLIMA)".

How to cite: Reñé, J. M., Udina, M., and Bech, J.: Analysis of the role of Saharan Dust Intrusions in PM10 precipitation scavenging in the NW Mediterranean Region, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-244, https://doi.org/10.5194/ems2024-244, 2024.

How to cite: Takane, Y., Nitta, T., Adachi, S. A., Yoshimura, K., Nakano, M., Nakayoshi, M., Onomura, S., and Crawford, B.: ILS+Urban: an offline land-surface process model for global urban climate and building energy simulations , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-316, https://doi.org/10.5194/ems2024-316, 2024.

The ongoing climate change results in increasing frequency of warm episodes and heat waves. While a lot of studies focuses on understanding and predicting outdoor air temperature and human comfort, less is known about indoor temperatures that are reached during these warm episodes. This is surprising since people generally spend the majority of their time indoors, e.g. at work or in their living or bed room. This study develops and evaluates a relatively simple forecasting system that aims to make five day forecasts for real world room temperatures. The system builds upon a relatively simple physical-statistical model for the heat budget of a room that is forced by outdoor weather variables like solar radiation, wind speed, air temperature. In addition, room temperature observations from ~60 houses in Amsterdam are used to train this physical-statistical model. After training the calibrated model is used for forecasting and driven by ECMWF operational forecasts of outdoor meteorological variables for the city of Amsterdam (The Netherlands) for the summer of 2023. Room temperatures are initialized daily by room temperature observations from these ~60 houses. We evaluate the forecasting system on the summer of 2023. We find the model system is well to produce a meaningful room temperature forecast for most of the houses. Forecasts for the daily mean temperature of the living room outperforms the forecast for the daily mean bed room temperatures. The median RMSE (over all 60 houses) for the living room forecast increases from 0.38 K for the one-day forecast to 0.95 K for the five-day forecast. Forecasts for daily mean temperatures are better than for daily maximum temperatures. With this forecast tool we aim to study whether citizens adapt their behaviour in protecting their homes from indoor heat when they receive the forecast information.

How to cite: Maas, A., Peerlings, E., and Steeneveld, G.-J.: “Weather forecasting” of indoor air temperatures in Amsterdam (The Netherlands) to facilitate early heat warnings., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-488, https://doi.org/10.5194/ems2024-488, 2024.

Urbanization has become a dominant factor in shaping local climate dynamics, manifesting profound impacts on the environment and human well-being. This effect is even more important over areas that are expected to become vulnerable to climate change in the future. Cyprus, which is located in the eastern Mediterranean and Middle East (EMME) region, is considered as a climate change “hot-spot”.

In this study, we employ the Weather Research and Forecasting (WRF) regional climate model driven by ERA5 reanalysis dataset coupled with different available urban parametrization schemes to simulate prevailing climatic conditions over the EMME and the island of Cyprus. The main objective of this research is to investigate the performance of -different in complexity- urban canopy models in simulating urban heat island over our region of interest, the capital city of Nicosia. The outcome of this investigation is the definition of the most suitable model set-up to be used over the area of Cyprus.

The simulations are performed at 1 km horizontal resolution for the year 2021, a year that Cyprus experienced a prolonged heatwave during summer, with 12 consecutive days of temperatures exceeding 40 degrees Celsius. We examine the seasonal variability of urban climate properties in Nicosia during this year.

The outcomes of this study provide valuable insights into the complex interactions between urbanization and climate. Understanding how different urban parametrization influence seasonal climate dynamics and selecting the best performing scheme aims in better representation of the local climatic conditions which can be further used in mitigation and adaptation assessments of the impacts of the ongoing global climate change.

How to cite: Constantinidou, K., Hadjinicolaou, P., Vurro, G., and Kushta, J.: Exploring Urban Climate Seasonal Dynamics over Cyprus with WRF Model: Implications of Urban Parameterizations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-533, https://doi.org/10.5194/ems2024-533, 2024.

The FOCI project (“Non-CO2 Forcers and their Climate, Weather, Air Quality and Health Impacts”, https://www.project-foci.eu/wp/) aims to better understand the impacts of key non-CO2 radiative forcers, to assess where and how they arise, and their impact on the climate system, with a final goal of providing tools to investigate mitigation and adaptation policies incorporated in selected scenarios of future development, targeted at Europe and other regions of the world. As part of the Work Package 4 from the FOCI project, which is responsible for tuning, testing and performing the long-term simulations with different regional models downscaling reanalysis and global climate models, we present an assessment of initial tests in WRF and RegCM5 with different domains over Europe. This study is divided into two parts: assessing the impact of 1) the chemistry in the 27-km domain that covers Europe, by comparing with a control simulation and a reference data, and 2) the nested 9 and 3-km domains on meteorological variables, such as precipitation, temperature, and horizontal winds. The 3 km convection-permitting (CP) simulations cover the city of Prague, one of the satellite cities from FOCI project, and an extreme weather event will also be evaluated. Data from ERA5 and assimilated chemistry by Copernicus Atmosphere Monitoring Service (CAMS) are used as boundary conditions, and E-OBS for validation the outputs. Some results show that simulations with chemistry tend to be slightly drier and warmer compared with no chemistry. Furthermore, although overestimating the precipitation over the Czech Republic territory, the 3-km CP simulation shows improvement in capturing the peaks of precipitation for the event.

How to cite: Machado Crespo, N., Villalba-Pradas, A., Verma, S., Karlický, J., Huszár, P., Belda, M., and Halenka, T.: Assessment of the horizontal resolution and an emission inventory in two different regional climate models in the FOCI project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-710, https://doi.org/10.5194/ems2024-710, 2024.

The trend of urbanization is increasing due to socioeconomic factors, leading to a decline in air quality and heightened exposure for individuals. Modeling dispersion of emissions within street canyons is crucial for understanding  local-scale air pollution exposure  indicators and devising effective strategies to enhance urban air quality. However, microscale urban air quality simulations are complex and involve a large number of interacting physical processes. Phenomena taken into consideration in simulations of urban pollution dispersion largely vary from one study to another. In this context, we aim at identifying the most influential parameters governing this phenomenon. 

First, we present a comprehensive database of pollution dispersion in street canyon by perturbing the most important parameters. In this study, these parameters include wind speed, background concentration, emission rates, background turbulence intensity, temperature disparity between building surfaces and the atmosphere, as well as model-specific parameters. We employ COMSOL Multiphysics for the computational fluid dynamic simulations. To keep feasible computational times and create a large database, turbulence is modeled by means of the Reynolds Averaged Navier-Stokes (RANS) k-ε technique and the domain is considered two-dimensional. 

This simulation bank is used to develop a surrogate model based on Gradient Boosting Machine and the Principal Component Analysis to reduce system complexity. This surrogate model offers a solution to carry out a multivariate global sensitivity analysis. To this end, we compute the Sobol Indexes, providing insights into the global contributions of input factors to the resulting pollution concentrations at each point of the physical domain. 

Results show that, as expected, background concentration dominates the pollution field far from the emission point. The temperature of building surfaces plays a secondary role, acting mainly on the interface between the street canyon cavity and the flow above the buildings. Emission plays a fundamental role, especially in the vertical concentration profile. Other  model-specific parameters have a minor role when compared to the other physical variables. Wind speed plays a key role, along with temperature, in determining how pollution exits the cavity.

By integrating advanced simulation techniques with rigorous sensitivity analyses, this study aims to provide valuable insights into the factors influencing air quality in urban street canyons. Such insights are crucial to perform model calibration, and to understand the implications of typical approximations such as non-buoyancy flows.

How to cite: Moreira Beltrami, G., Beicker, R., Gonçalves dos Santos, R., Jorba, O., and Mateu Armengol, J.: Sensitivity analysis in pollution dispersion within street canyons, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-763, https://doi.org/10.5194/ems2024-763, 2024.