Regional reanalyses are now available for Europe and specific sub-domains, e.g. produced by national meteorological services. Global and regional reanalyses are an important element of the Copernicus Climate Change Services.
The interest in extracting climate information from reanalysis is rising and they are used in a wide range of applications. Due to their frequent use in energy meteorology, they also play a key role for this year's conference theme “weather and climate research in the achievement of a climate-neutral Europe”. Recently, it has become apparent that reanalyses are a popular basis for training in machine learning methods that enable successful AI-based weather forecasts, for example.
This session invites papers that:
• Present the status of reanalysis activities in Europe and beyond.
• Explore and demonstrate the capability of global and regional reanalysis data for climate applications, including energy applications.
• Illustrate the role of reanalysis data for machine learning and artificial intelligence.
• Compare different reanalysis (global, regional) with each other and/or observations
• Improve recovery, quality control and uncertainty estimation of related observations
• Analyse the uncertainty budget of the reanalyses and relate to user applications
Regional reanalysis data sets are becoming more used and requested for a broad spectrum of users, from climate adaptation and mitigation, to agricultural and economical applications (e.g. energy meteorology). While the development of a reanalysis system mainly relies on an existing numerical weather prediction (NWP) model and corresponding data assimilation scheme, it involves a large amount of testing from both a computational and technical perspective.
The talk will present preliminary results of the development for a new pan-European regional reanalysis at the Deutscher Wetterdienst (DWD). The framework involves the ICON NWP model in its global configuration with a two-way coupled nest over Europe and its operational data assimilation method run at 3-hours intervals, namely EnVar for the deterministic run at 13 km global resolution (6.5 km over Europe) and Localized Ensemble Transform Kalman Filter (LETKF) for the 20 ensemble members at 40 km global resolution (20 km over Europe).
The reanalyses cover the 2010-2022 period, extended to current, and it is run split in 3 streams (June 2009, January 2015, and January 2018) with forward overlap. The first stream start date includes a 6-months initial spin-up, that in the other two streams is dealt through the forward overlap. The spin-up phase is necessary for the soil to reach the equilibrium, and in our case is shorter than what usually seen in free-running simulations because our system performs a surface analysis for soil-moisture every day at 00UTC.
First of all, we present that the number of active observations has a similar magnitude to what shown for ERA5. Then we proceed showing the evolution of the profiles averaged over Europe of first-guess and analyses departures w.r.t. observations.
Preliminary results of the first year of the 2018 stream are evaluated in observation space on a 6-day average over the European domain. First we see that first-guess bias for the radiosondes and dropsondes (TEMP) temperature of at most -0.12K below 500 hPa and +0.3 K above it. The bias magnitude is reduced by at least half when the analysis is used. The SYNOP biases for the 2-meter temperature and relative humidity are on average zero, with spatial variability maxima of 0.2 K/0.01 (first guess) and 0.1 K/0.005 (analysis).
How to cite: Valmassoi, A., Keller, J. D., Potthast, R., Anlauf, H., and Cress, A.: Towards a new generation of regional reanalyses for Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-27, https://doi.org/10.5194/ems2024-27, 2024.
The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution. Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.
For our new reanalysis system only observation data of the ECMWF ERA5 reanalysis will be used. To provide these data to the operational DWD assimilation scheme a converter has been developed. Test experiments have been performed to evaluate whether the current system performance can be improved by using ERA5 observation datasets instead of the operationally used observations from DWDs data base. For testing we use the Basic Cycling Environment (BACY) of DWD being characterized by different modules for each cycling step (e.g boundary data, ICON run, assimilation scheme). BACY enables users to perform cycling over multiple time steps (automatization) as well as repeated tests of a single module for debugging or binary checks. Here we present the BACY specifications chosen for the reanalysis production as well as results of the assimilation experiments.
How to cite: Kelbch, A., Valmassoi, A., Spangehl, T., and Borsche, M.: Ensemble-based regional reanalysis system for Central Europe: Assimilation experiments with ERA5 observations, data conversion, results and outlook, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-179, https://doi.org/10.5194/ems2024-179, 2024.
The development and provision of regional reanalyses requires a coordinated quality assessment targeted to user needs. Regional reanalyses with higher temporal and spatial resolution are of particular importance for applications in the energy sector. Detailed information is required on meteorological parameters such as wind speed and direction at hub heights of modern wind turbines (offshore and onshore), global radiation and near-surface temperature. In addition, regional reanalyses are of interest for climate applications dealing with climate extremes such as strong wind, heat waves, heavy precipitation and droughts.
The regional reanalysis COSMO-R6G2 is currently produced at DWD as a successor of COSMO-REA6. It is based on a newer version of the COSMO model and ERA5 boundary conditions, which enables continuous updating. The nudging technique (Newtonian relaxation) is used for the assimilation of conventional observations stemming from radiosondes, SYNOP stations, ships, buoys or aircrafts. No satellite data is assimilated.
The quality is assessed using in-situ measurements of offshore FINO research platforms, tower and lidar measurements over Germany (companion paper by Bär et al., EMS 2024), DWD’s station network for Germany and satellite-based surface irradiance data of CM SAF as reference. Uncertainties are addressed by a comprehensive comparison to other state-of-the-art reanalyses and derived products.
The temperature and radiation biases seen in COSMO-REA6 are still similarly present in COSMO-R6G2. However, there is evidence that the representation of reduced shortwave transmissivity related to the effect of optically thick clouds is more realistic in COSMO-R6G2 compared to COSMO-REA6 where post-processing methods have already been proven for bias adjustment of non-cloudy cases.
How to cite: Spangehl, T., Borsche, M., Niermann, D., Bär, F., Drücke, J., Kelbch, A., Kaspar, F., Imbery, F., and Becker, A.: Evaluation framework for regional reanalyses at DWD: results and outlook, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1020, https://doi.org/10.5194/ems2024-1020, 2024.
The ARRA (ARome ReAnalysis) project was launched at Météo-France in 2022, in order to replace the old existing reanalysis system SAFRAN by a system based on the numerical weather prediction (NWP) system AROME. The objective is to cover a period of 60 years at 1.3 km over France with the last version of the operational AROME system, and produce surface and upper air variables , which are of interest for many uses, especially in the energy sector. There is a strong interest in producing this reanalysis with a recent version of AROME in order to promote the joint use of the results of this reanalysis with real-time analysis or forecasting products , as is currently the case with SAFRAN. The production of this reanalysis is part of Météo-France's 2022-2026 Contract of Objectives and Performance (COP), which sets out the institution's strategic directions and objectives for the next five years to better meet the expectations of citizens, the French government and the business community, each of them facing the challenges of climate change at its own level. With this new reanalysis, small-scale phenomena, such as convection or orographic wind circulation, will be significantly improved compared to the existing SAFRAN system or the European Copernicus CERRA reanalysis, produced at 5.5 km resolution. Beyond the replacement of SAFRAN-based products and systems, this new reanalysis is also necessary for the post-processing of the next generation of kilometer-scale climate projections using AROME. It will also constitute a key dataset for the further use and deployment of machine learning (and other AI methods) for high- resolution weather and climate applications.
The ARRA reanalysis will have two components: the atmospheric part with a 3-hour cycle for the surface analysis (air temperature and humidity at 2 meters, snow and soil moisture) associated with the Increment Analysis Update (IAU) technique based on CERRA or UERRA reanalysis for the upper air and a specific product for the surface called ARRA-Land also at 1.3 km. ARRA-Land will be the SURFEX offline surface model driven by ARRA atmospheric forcing fields and by a 24-hour cumulative precipitation analysis based on MESCAN (Soci et al. 2016). The ARRA-Land product will contain hourly surface fields to produce estimates of surface and soil conditions (soil moisture, snow, etc.), with more advanced physics for the surface than in ARRA.
After a brief description of the project and system configuration, preliminary comparison with other reanalysis (SAFRAN, UERRA, CERRA) will be presented and discussed.
How to cite: Bazile, E., Le Moigne, P., Selly, Y., Van Hyfte, S., and Willemet, J.-M.: ARRA: A kilometer-scale reanalysis over France with AROME, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-193, https://doi.org/10.5194/ems2024-193, 2024.
The continuous breaking of temperature and rainfall records worldwide is a stark reminder that climate is changing. These record-breaking events underscore the urgent need to understand and adapt to these changes as they can lead to more frequent and severe weather events, e.g. heatwaves (or cold waves), extreme (localised) rainfall, and (flash) floods. Hence, investing in sustainable practices, enhancing forecasting and response strategies, and implementing policies to mitigate climate change effects is critical. But to do so, we need appropriate data to inform our decisions.
Observation-based climatologies help detect trends and patterns in the climate over a long period of time and can contextualise extreme, high-impact weather events. However, observations can be inaccurate and unevenly distributed in space and time. Reanalysis such as ERA5 provides a good alternative to observation-based climatologies because it provides accurate, temporally consistent, gridded estimates of the past state of the Earth system worldwide. However, this only satisfies some needs due to ERA5's relatively coarse model resolution (precluding the representation of localised extremes) and some intrinsic biases.
How to cite: Pillosu, F., Hewson, T., and Cagnazzo, C.: ERA5-ecPoint: what it is and its power in the contextualisation of severe temperatures and rainfall, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1065, https://doi.org/10.5194/ems2024-1065, 2024.
Wind speed data from reanalysis models is used for a wide range of industrial and research applications in the wind energy sector. These range from resource estimation in the development phase to planning of the future (wind) energy system and the integration of wind energy into electrical grids. Validation of reanalysis models for offshore conditions far away from the coast is relatively straightforward, for which very good agreement between reanalysis models and wind speed measurements has been demonstrated. In contrast, validation in heterogeneous and complex terrain is much more difficult.
This study uses an extensive measurement dataset with high-quality wind speed measurements at heights relevant for modern wind energy applications (100 – 200 m) to evaluate and compare a set of reanalysis models, i.e. ERA5, COSMO-REA6, CERRA, and NEWA. The evaluation dataset in this study comprises approximately 100 lidar and mast measurements, mainly carried out for wind park developments, distributed over Germany and covering a wide range of topographic conditions.
The analysis focuses on identifying local topographic effects and indicators that influence the quality of the agreement between the reanalysis models and the measurements (i.e. correlation) as well as systematic deviations (biases). The investigated topographic effects include orographic exposure, land /forest cover, and sub-grid scale orography. In a preliminary analysis based on 44 measurement stations, a clear correlation between the bias in the reanalysis models (compared to the measurements) and the orographic exposure of the measurement location could be demonstrated. At measurement locations with ground heights exceeding the height of the grid cell of the reanalysis model, the model data underestimated the observed wind speeds. The opposite could be observed at measurement location below grid height.
These findings are especially important for wind energy applications, as wind farm developments tend to concentrate on areas with high exposure and specific land use types. Moreover, the observed relationships with local geography potentially provide the possibility to empirically correct and downscale wind speeds from reanalysis models. This allows to better represent the wind resource available for wind parks and to estimate the uncertainties based on local geographical conditions.
How to cite: Pauscher, L., Geiger, D., Yuan, D., Bär, F., Good, G., Spangehl, T., Kaspar, F., Weber, H., and Callies, D.: An evaluation and comparison of wind speeds from different reanalysis models in the context of wind energy – the influence of topography, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-957, https://doi.org/10.5194/ems2024-957, 2024.
Country-wide energy systems analysis requires the simulation of wind power feed-in. This is usually derived using wind turbine data and wind speed time series from reanalysis models. As reanalysis models exhibit biases especially in complex terrain, the modelled feed-in deviates from the observed wind energy feed-in. In this work, we assess several state-of-the-art reanalysis models such as REA6, ERA5, CERRA and NEWA by simulating all current German windfarms and comparing the results with country-wide feed-in time series from ENTSO-E or SMARD. In a second step the wind turbine model parameters are calibrated for each reanalysis model to better match the observed capacity factors.
In order to simulate the wind power feed-in, we use a regionally smoothed power curve to model feed-in and an empirical shading curve to account for intra park turbine wakes. The location, hub height, rotor diameter and installed capacity are taken from the Marktstammdatenregister (MaStR) with missing parameters estimated from wind turbines with similar characteristics. The total installed capacity is corrected or scaled using the ENTSO-E and SMARD data. If available, manufacturer power curves are used; otherwise, synthetic power curves are applied. Additionally, other loss factors such as technical availability are included using constant factors.
In the first analysis the feed-in model is parameterized with standard values to assess the general performance of each reanalysis model. The obtained results are analyzed with respect to capacity factors, bias, and correlation coefficients, providing an indication of the overall performance of the reanalysis models in terms of wind energy generation.
In the second step, the analysis framework is used to derive calibration factors for the feed-in model, aiming to achieve more realistic results for each of the reanalysis models. By calibrating the wind turbine model parameters, we aim to reduce the difference between the modeled and the observed capacity factors.
The performed analysis and calibration highlight the need for an improved understanding of the performance of reanalysis data sets for wind energy system modelling and the development of more sophisticated corrections.
How to cite: Geiger, D., Pfennig, M., Callies, D., Pape, C., and Pauscher, L.: Assessing the performance of reanalysis models for wind power modelling in Germany , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1038, https://doi.org/10.5194/ems2024-1038, 2024.
In recent years, attention has intensified on the interplay between environmental stressors, notably poor air quality, heatwaves, droughts, and fires, as exacerbated by climate change. These stressors, once viewed in isolation, are now recognized as interconnected phenomena forming a complex web of impacts with global repercussions. The compounding effects of these events have significant implications for ecosystems, economies, and public health worldwide. In this work, we present a comprehensive analysis of compound events of poor air quality, heatwaves, droughts, and fires on a global scale, shedding light on their interconnected nature, underlying drivers, and implications for ecosystems and societies worldwide.
We use meteorological data from ERA5 to compute heatwaves and droughts (using the Standardized Precipitation-Evapotranspiration Index – SPEI), Fire Radiative Power (FRP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua sun-synchronous orbit satellites, and air quality data (particulate matter PM2.5) retrieved from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis (EAC4). The horizontal resolution is preset at 0.75º × 0.75º, and the time window is between 2003 and 2022 at a daily basis. Compound events were identified based on the detection of pollution events, heatwaves, droughts, or FRP in various combinations, ranging from two to four events, within each cell of space and time.
Analysis revealed hotspots of compound events distributed across different regions worldwide. For instance, pollution and heatwaves were predominantly observed in India, the Arabian Peninsula, and eastern China, whereas heatwaves and fires were prevalent in the Brazilian Cerrado, northern Australia, and South African Savannas. The impacts of single and concurrent hot, dry, and fire events on particulate matter PM2.5 levels varied significantly by continent. Notably, North America and Asia exhibited pronounced pollution levels during simultaneous occurrence of these events compared to isolated pollution events.
The interaction between compound hot and dry events and wildfires poses a critical public health challenge, underscoring the interconnectedness of climate change, extreme weather events, and air pollution. Addressing these complex interrelationships necessitates comprehensive strategies integrating climate resilience, wildfire management, and air quality regulations to safeguard human health and well-being amidst a changing climate.
This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). This work was performed under the scope of project https://doi.org/10.54499/2022.09185.PTDC (DHEFEUS) and supported by national funds through FCT. DL and AR acknowledge FCT I.P./MCTES (Fundação para a Ciência e a Tecnologia) for the FCT https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004 and https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006, respectively.
How to cite: Bento, V. A., Lima, D. C. A., Careto, J. A., and Russo, A.: Global Assessment of the Compound Effects of Hot, Dry Conditions and Fires on PM2.5 Levels using ERA5 and CAMS reanalysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1085, https://doi.org/10.5194/ems2024-1085, 2024.
The critical challenge of climate change in the Eastern Mediterranean and Middle East (EMME) region, especially on the island of Cyprus, is closely linked to the emissions of greenhouse gases and the dispersion of air pollutants. This study utilizes reanalysis gridded data from multiple open data platforms, including the Copernicus Atmospheric Monitoring Service (CAMS), providing unprecedented access to large-scale datasets specifically focused on emissions and pollutants within this region. By analyzing different gridded datasets, the research aims to develop a detailed understanding of the spatial and temporal patterns of emissions, dispersion processes, and their interplay with atmospheric conditions across various sectors.
The approach involves data aggregation from diverse open sources, employing advanced analytics to decode complex spatial and temporal variations, and comparative assessments to identify discrepancies and trends between reanalysis data and actual observations. This study will assess how reanalysis climatic factors contribute to the variability in emissions and pollutant levels, thereby evaluating the environmental impacts specific to various sectors and the effectiveness of current mitigation strategies throughout the EMME region, with a particular emphasis on the situation in Cyprus.
Although preliminary results are yet to be generated, the expected findings aim to offer significant insights into the dynamics of emissions and pollution dispersion within this region. This investigation not only highlights the critical role of open and reanalysis data in environmental research but aims to make a substantial impact on regional discussions about sustainable practices by offering practical, data-driven insights. Furthermore, this study will explore how these findings can inform and enhance regional climate policy and action plans. It will particularly demonstrate how reanalysis data can be pivotal in understanding and tackling the complex issues of climate change in the EMME region, making it an essential contribution to the field of spatial climatology and regional environmental policy-making.
How to cite: Christodoulou, K.: Comparing Reanalysis and Observational Data for GHG Emissions and Air Pollutants in the EMME Region, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-836, https://doi.org/10.5194/ems2024-836, 2024.
Reanalyses are currently a major source of information about the state of the troposphere in many meteorological and climatological investigations. However, since they are a product of numerical modeling, they contain some systematic errors, which occur mainly due to the discretisation of a domain, parametrisation of physical processes and numerical approximations. The errors are particularly apparent for meteorological variables which cumulate over time and have great spatial heterogeneity. One example of such a variable is snow depth. In this work, snow depth from ERA5 and ERA5-Land reanalyses was evaluated against observations from over 300 stations in Poland, Czech Republic and Slovakia in winter seasons from 2001 to 2021. Additionally, an attempt was made to reduce existing biases and produce the field in finer resolution based on terrain characteristics derived from a digital elevation model. Verification results show that the difference between modelled and real elevation, together with station rank, are major factors contributing to systematic errors. In complex terrain, bias occurs nearly at every station, while in the lowlands, it is roughly neutral at synoptic stations as their data are assimilated during the production of the reanalyses. For lower-ranked stations, errors are apparently higher. On average, ERA5-Land has lower Root Mean Square Error (RMSE) than ERA5, however, there are stations with greater positive and negative bias. Despite higher resolution, ERA5-Land performs worse than ERA5 for around 30% of stations. Machine learning methods are capable of reducing systematic errors existing in the reanalyses. The greatest improvement occurs especially for sites in complex terrain. Statistical downscaling provides some useful insight in spatial distribution of snow depth, however, its physical consistency needs to be enhanced, e.g., by using physically-constrained machine learning.
How to cite: Stachura, G.: Evaluation and machine-learning-based downscaling of ERA5 snow depth in Central Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-168, https://doi.org/10.5194/ems2024-168, 2024.
Meteorological reanalyses are used for multiple applications, from understanding past climate to assessing extreme weather events. In this study, an extensive validation of high-resolution regional reanalyses was performed, evaluating their capability to reproduce precipitation fields over Italy, which is characterized by complex orography and land-sea interactions.
In this study, we carried out an inter-comparison of nine different reanalysis products, using the ECMWF ERA5 product as the global reanalysis reference. Some of the reanalyses considered cover Europe (BOLAM, COSMO-REA6, CERRA), while others are specifically designed for Italy (MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT), using a variety of different atmospheric models and parametrizations.
The precipitation fields inter-comparison employed wavelet decomposition techniques and frequency distribution analysis. Then, a validation against independent observations involved both observation products interpolated onto regular grids and punctual data from stations’ series.
The wavelet decomposition permitted assessing the effective information of every reanalysis at each spatial scale, clustering the products into global, regional, and convection-permitting ones. On the other hand, the frequency distribution of daily rainfall amounts allowed proving the capability of higher-resolution reanalyses to depict the frequency of occurrence of extreme precipitation events better than the ERA5 global product.
Moreover, the comparison of the reanalyses fields with observations was performed by calculating the relative bias to assess climatology reliability and the Stable Equitable Error in Probability Space (SEEPS) to evaluate the ability to discriminate among dry, light rain, and heavy rain days. The results indicated wet biases over the Alps, especially during spring and summer, and dry biases over Liguria, Tuscany, the Apennines, and Southern Italy during autumn and winter. Significant differences were found among reanalysis products, with MOLOCH and MERIDA-HRES showing the best performances.
Finally, the trend analysis revealed a long-term signal in the reanalysis precipitation bias, with a significant increase in the annual amount in most reanalyses (with the only exception being VHR-REA_IT), suggesting caution when using these products for climate change studies in this area.
How to cite: Cavalleri, F., Lussana, C., Brunetti, M., Viterbo, F., Bonanno, R., Manara, V., Lacavalla, M., and Maugeri, M.: Multi-scale assessment of regional high-resolution reanalyses precipitation fields over Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-9, https://doi.org/10.5194/ems2024-9, 2024.
The interest towards the development of regional high-resolution retrospective datasets, allowing an enhanced representation of past meteorological states, has been progressively increasing. Convection-permitting (CP) datasets (i.e., hindcasts, based on historical model integrations, or reanalyses, including the additional assimilation of historical observational data) have demonstrated to improve the representation of precipitation compared to convection-parameterized counterparts. The benefits involve the spatial structures of rainfall fields, the timing and peak of the diurnal cycle of summer precipitation, and the frequency of wet days/hours. This is of particular relevance for enhancing the characterization of severe precipitation events with the aim to prevent and minimize their impacts on terrestrial ecosystems, and on human and animal life. However, the simulation of convective-related phenomena is highly model-dependent, implying the inability to sample the full range of natural variability with single-model experiments. This is exacerbated for km-scale simulations owing to the intrinsic chaotic behavior underlying convective processes.
Recently, the development of Multi-Model Ensembles (MMEs) of CP regional climate models over Europe has demonstrated to efficiently tackle this issue and reduce the simulation error associated with single model outputs. This approach could benefit also retrospective estimates in order to retrieve a complete, homogeneous, and optimized assessment of past atmospheric states. In case of precipitation, this could be valuable also for potential downstream modeling applications such as forcing hydrological forecasting systems to obtain improved historical series of high-impact flood events.
This work presents the first MME of retrospective CP datasets over Italy based on four reanalyses/hindcasts recently produced, with the aim to assess the added value of their joint employment. The datasets are obtained by dynamically downscaling the global reanalysis ERA5 using different numerical models: MERIDA-HRES (based on WRF-ARW), the hindcast based on the model MOLOCH, and SPHERA and VHR_REA-IT (both based on COSMO). The reference dataset for comparison is GRIPHO, the first Italian pluviometer-based hourly analysis. The investigation over a decade (2007-2016) includes various aspects such as the annual and seasonal dependence of daily and hourly mean rainfall intensity and frequency, heavy precipitation occurrences, and their summer diurnal cycles. The results indicate that a superior dataset performing always best is not detected, while large inter-model variability characterizes summer precipitation. The ensemble aggregation systematically improves rainfall estimates over single datasets, resulting in more adherent spatial fields and lower root-mean-squared errors and relative biases when compared to the observations, at the expense of reduced spatial variability of the distributions.
How to cite: Giordani, A., Ruggieri, P., and Di Sabatino, S.: Added value of a multi-model ensemble of convection-permitting rainfall re-forecasts over Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-761, https://doi.org/10.5194/ems2024-761, 2024.
In 2023, renewable energies contributed to 56% of the total electricity production in Germany. A further increasing of this percentage is expected in the near future due to rising number of wind and solar power installations. Their production is closely intertwined with climatological conditions and tied to local meteorological conditions.
In order to estimate and investigate potentials for the generation of these renewable energies’, consistent datasets on a climatological scale are required. In recent years, reanalyses have proven to be a reliable source providing long-term consistent meteorological data sets for use in the energy sector. For such applications Germany's National Meteorological Service (Deutscher Wetterdienst, DWD) currently produces a new regional reanalysis product, the “COSMO-REA6 Generation 2” (COSMO-R6G2). The new data set is a successor of the well-known COSMO-REA6 reanalysis (developed by DWD in cooperation with the universities of Bonn and Cologne within the Hans-Ertel-Centre for Weather Research of DWD), but using ERA5 instead of ERA-Interim as boundary conditions. COSMO-R6G2 will cover the period from 1995 to present, retaining the spatial and temporal resolution of COSMO-REA6.
A systematic quality assessment is required before the data set is applied in energy-related projects of the DWD and released to the public.
For example, DWD is involved in the ‘Network of Experts’ of the German Ministry of Digital and Transport (BMDV), where the energy potential in the German transport infrastructure is analysed. Another example is the project MEDAILLON (supported by the Ministry of Economic Affairs and Climate Action (BMWK)), which aims to provide datasets for energy system research).
This contribution focuses on the evaluation of wind speed and direction at typical hub height of wind turbines of about 100 m a.s.l. over the German land area. The quality of different reanalyses and derived data sets for national offshore wind applications has recently been demonstrated by Spangehl et al., 2023 (https://doi.org/10.5194/asr-20-109-2023). To assess the usability for onshore wind energy applications we compare R6G2 wind fields to ground-based LIDAR measurements and wind measurement masts in Germany. Moreover, we compare the performance of COSMO-R6G2 compared to other reanalysis data sets, e.g. ERA5, COSMO-REA6 to identify potential improvements such as the more realistic representation of the diurnal cycle.
How to cite: Bär, F., Borsche, M., Geiger, D., Kaspar, F., Kelbch, A., Pauscher, L., Spangehl, T., Weber, H., and Yuan, D.: Evaluation of reanalyses for onshore wind energy applications in Germany, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1081, https://doi.org/10.5194/ems2024-1081, 2024.
The aim of CERISE is to develop new and innovative coupled land-atmosphere data assimilation approaches and land initialisation techniques to pave the way for the next generations of the Copernicus Climate Change Service (C3S) reanalysis and seasonal prediction systems. These developments are combined with innovative work on machine-learned observation operator to ensure optimal data fusion fully integrated in coupled assimilation systems. The project aims at improving the quality and consistency of the C3S reanalysis systems and of the components of the seasonal prediction multi-system, directly addressing the evolving user needs for improved and more consistent C3S Earth system products.
This presentation gives an overview of the objectives of the CERISE project with a focus on developments of coupled land-atmosphere data assimilation to improve the climate consistency of the next generation of C3S Earth system global and regional reanalyses. It describes ensemble-based unified land data assimilation and coupling infrastructure and methodology developments conducted in the first 18 months of the project. Work on machine-learning based observation operator to enhance the exploitation of passive microwave data is introduced, presenting the training databases, the machine learning approaches developed, and results comparing simulated and observed brightness temperature from AMSR2. Results from numerical experiments show the benefits of using ensemble-based land data assimilation for surface and near-surface weather representation both at regional and global scales. The first CERISE global land reanalysis prototype is presented. Its results are compared to state-of-the-art operational reanalysis using a set of newly developed diagnostic tools. Infrastructure, methodology and scientific results presented highlight the feasibility and the added value of the integration of the CERISE developments in the existing C3S core service.
How to cite: de Rosnay, P., Aires, F., Bojarova, J., Brookshaw, A., Calvet, J.-C., Cardinali, C., Herbert, C., Hersbach, H., Knight, J., Pérez-Zanón, N., Schyberg, H., Stockdale, T., Trigo, I., Vitart, F., and Weston, P.: Coupled land-atmosphere data assimilation developments in support of the next generation of Earth system reanalyses and seasonal prediction systems: The CopERnIcus Climate Change Service Evolution (CERISE) project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-573, https://doi.org/10.5194/ems2024-573, 2024.
