Mediterranean cyclones are high-impact weather events that frequently result in devastating floods, storm surges, and windstorms, sometimes leading to casualties. They may exhibit characteristics typical of tropical (or sub-tropical) cyclones (e.g., a warm core, a cloud free eye surrounded by spiraling rain bands around the center, and a closed vortex associated with strong near-surface winds and heavy precipitation). Less frequently, these cyclones undergo transition into a tropical-like cyclone (TLC) during their mature phase, exhibiting at some point during their evolution a deep axisymmetric warm core of diabatic origin (i.e., latent heat release due to air-sea interaction and moist convection). The latter cyclones are generally referred to as Medicanes (Mediterranean Hurricanes). However, the term Medicane is often associated with other types of warm core cyclones, including warm seclusions present in the late stage of extra-tropical cyclones, where the warm core originates from baroclinic processes. The present work presents some recent advancements in the use of satellite passive microwave (PMW) measurements to monitor and to characterize warm core, deep convection and the presence of a closed eye during the cyclone evolution in order to identify the possible transition into TLC. Moreover, all the available scatterometers onboard LEO satellites (MetOp ASCAT and FY-3E WindRAD) are used to monitor the evolution of the surface wind field as the cyclone evolves to the mature stage and its relation to the cyclone intensification. The analysis is carried out for 15 Mediterranean cyclones that occurred in the last 21 years (2003-2023) and reveals that only 9 of them underwent a TLC transition during their mature phase. In particular, the study focuses on three cyclones (i.e., Helios, Juliette, and Daniel) that occurred between February and September 2023. The results indicate that the three cyclones show a very similar evolution during the initial phases, characterized by a dry stratospheric air intrusion followed by the development of a warm anomaly in the low/mid-troposphere around the cyclone center. This phenomenon is clearly driven by baroclinic processes. However, while for Helios the PMW diagnostics do not show deep convection in the warm core region, for both Juliette and Daniel deep convection is identified in the warm core region at the final stage of their mature phase, providing a strong indication that diabatic heating plays a key role in the warm core development. From the analysis, it can be concluded that, while Helios is a warm seclusion, Juliette and Daniel undergo a TLC transition at the final stage of their evolution. This research is an important contribution towards the use of Earth Observation for Medicanes’ definition, within the activities of the ESA MEDICANES project and of the COST Action MedCyclones.
How to cite: Panegrossi, G., D'Adderio, L. P., Sanò, P., Casella, D., Sebastianelli, S., Rysman, J.-F., Dafis, S., Miglieta, M. M., Di Francesca, V., and Herndon, D.: Satellite-based characterization of Mediterranean tropical-like cyclones (Medicanes), 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-39, https://doi.org/10.5194/egusphere-plinius18-39, 2024.
Accurate weather forecasts are important to our daily lives. Wind, cloud and precipitation belong to the fundamental variables in NWP models. The WIVERN (Wind Velocity Radar Nephoscope) mission (Illingworth et al., 2018), for observing global winds, clouds and precipitation, has the opportunity to be the first space-based mission to provide in-cloud winds. It is currently in the phase A of the European Space Agency (ESA) Earth explorer 11 program and, if demonstrated successful, WIVERN data could be beneficial to enhance NWP performance, to improve our knowledge of weather phenomena, and to validate climate statistics.
In this work, we present an assimilation experiment of the WIVERN Doppler (HLoS; Horizontal winds along the Line of Sight) data for the outstanding case of the Medicane Ianos, occurred in mid September 2020.
To this end, we use the following approach: we run the Medicane Ianos with WRF at 4km horizontal resolution using the ECMWF-EPS (European Centre for Medium range Weather Forecast – Ensemble Prediction System) analysis/forecast cycle issued at 12 UTC on 16 September 2020 as initial and boundary conditions. Fifty-one occurrences of the Medicane Ianos (members) are forecast, taking into account for the atmospheric predictability of that day. For all members the trajectory of the Medicane is determined by the minimum surface pressure.
The trajectories are then compared with the reference trajectory determined by the method of Flaounas et al. (2023) that makes use of ERA5 reanalysis and the best member among the 51 WRF simulations is determined. The best member is that minimizing the spatial error compared to the reference trajectory. WIVERN pseudo-observations are then generated for the best member using the Wivern simulator (Battaglia et al., 2022). Pseudo-observations are then assimilated into the WRF model every 3h using the 3DVar scheme of Federico (2013). Results show a positive impact of the data assimilation on the simulation of the Ianos trajectory. The distance between the simulations assimilating HLoS and the best trajectory are more than halved compared to the control forecasts.
Sensitivity tests to the observation error and to the WIVERN revisiting time show that the latter has a much larger impact on the quality of the forecast.
References
Battaglia, A., et al., 2022, https://doi.org/10.5194/amt-15-3011-2022.
Federico, S., 2013, https://doi.org/10.5194/amt-6-3563-2013.
Illingworth, A. J., et al., 2018, DOI: 10.1175/BAMS-D-16-0047.1, 1669-1687.
Flaounas, E., et al., 2023, https://doi.org/10.5194/wcd-4-639-2023
How to cite: Federico, S., Torcasio, R. C., Montopoli, M., Panegrossi, G., Cambiotti, C., and Battaglia, A.: Assimilation of WIVERN Doppler data in WRF model for the case study of Medicane Ianos, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-20, https://doi.org/10.5194/egusphere-plinius18-20, 2024.
Extreme precipitation events pose significant challenges, especially in semi-arid regions where climate change has increased the frequency of such episodes, exacerbating the lack of adequate infrastructure to mitigate their impacts. The integration of satellite data with modeling techniques emerges as a crucial strategy for characterizing and predicting these events effectively. The Spain’s floods in September 2019 serve as a poignant example, resultingin in 7 casualties and 19 million euro in damages. The more affected regions were the Valencian Community, the Region of Murcia, Castilla-La Mancha and Andalusia, with areas in the south of the Community of Madrid also experiencing significant impacts by the end of the episode.
An analysis comparing satellite data with outputs from Numerical Weather Prediction (NWP) and simple hydrological models, alongside direct observations such as METEOSAT data, rain gauges and ground Doppler radars, reinforces the critical role of satellites in managing hydrometeorological events effectively. The Global Precipitation Measurement (GPM) Core Observatory, operational since 2014, and merged satellite estimates have demonstrated remarkable improvements over previous technologies. Additionally, the timely availability of satellite estimates enables near-real-time monitoring of severe hydrometeorological episodes.
While future automation of models remains a goal, current reliance on satellite products such as Integrated Multi-satellitE Retrievals (IMERG) can significantly aid in addressing societal needs. The ultimate objective is to transition from observation-based responses to predictive capabilities, with satellite data playing a central role in this transformation.
How to cite: Leganes, L., Navarro, A., and Tapiador, F.: An example of the combined use of satellite data and models for the analysis of extreme precipitation events in the Mediterranean: The September 2019 floods in Spain, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-23, https://doi.org/10.5194/egusphere-plinius18-23, 2024.
The Mediterranean region, recognized as a climate change hotspot, faces increasing and intensity of extreme weather events, posing significant challenges to vulnerable communities. In response we propose a novel artificial intelligence (AI) model designed to predict two distinct extreme weather phenomena in the Mediterranean basin: major Medicanes (across the entire basin) and severe coastal winds (tested in the Valencian Community). Our model employs a hybrid AI framework, adapted from former work on tropical cyclone forecasting rooted in a binary classification method, to enhance forecasting capabilities for these heavily non-linear extreme weather events, especially under rapidly evolving conditions. Our methodology involves analyzing datasets associated with each weather phenomenon, categorizing them into extreme and non-extreme based on the maximum wind speeds. By leveraging infrared satellite imagery and atmospheric reanalysis data, we extract critical features preceding the peak intensity of these events. These features enable the prediction of extreme wind conditions up to two days in advance in both cases. For the Medicanes study, our AI model successfully predicted 65-80% of extreme cases. The predictive accuracy of Western Mediterranean coastal winds averaged a precision of 85% for the region. This innovative and versatile methodology can be adapted to diagnose various severe weather phenomena beyond Medicanes and coastal winds, offering a robust tool for climate change adaptation strategies. Our research contributes to a deeper understanding and better management of nonlinear, climate-influenced weather events in the Mediterranean region.
How to cite: Nieves, V., Martinez-Amaya, J., and Guerrero-Navarro, G. H.: AI-Driven Predictions: Foreseeing Mediterranean Extreme Weather in a Changing Climate, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-44, https://doi.org/10.5194/egusphere-plinius18-44, 2024.
Air temperature (Ta) plays a crucial role in numerous applications, including studies on physical stress conditions and understanding phenomena such as urban heat island (UHI). Due to the increasing frequency of summer heat waves caused by climate change, the UHI, which in itself leads to a significant rise in nighttime temperatures in cities, can cause extreme physical discomfort for humans, especially during these periods. These heat waves, like heavy rains and strong winds, belong to the category of extreme weather phenomena, and combined with the UHI, they can lead to critical conditions in cities during the summer. For this reason, it is necessary to study and accurately characterize the UHI phenomenon. This study employs innovative techniques to achieve this goal, also with a view to studying mitigation strategies to address increasingly critical summer conditions in cities.
Ta measurements, acquired from in situ sensors often distributed unevenly, are limited in describing the spatial temperature field pattern. On the other hand, land surface temperature measurements (LST) obtained from geostationary satellites provide a more detailed spatial overview, but represent a different variable. In this work, a method based on machine learning algorithms is presented for converting LST detected from geostationary satellites MSG, into air temperature. To perform the conversion, a gradient boosting algorithm, which is part of the tree-structured family of machine learning algorithms, was implemented. The method is applied to LST and Ta data available for the city of Rome (Italy) during the summers of 2019 and 2020. The Ta data are sourced from 17 weather stations, predominantly consisting of amateur stations whose quality has been verified. Using predictive variables such as instantaneous LST and with delays ranging from 1 to 4 hours, along with other parameters like altitude, imperviousness, land cover, tree cover, grassland, NDVI, and temporal parameters such as time of day, Ta was estimated, designated as the target variable, at points where no in situ measurement sensors are available. The Ta predicted by the model exhibits an average error of 1.2°C during the daytime and 0.8°C at night. This model output has improved the accuracy and spatial resolution of temperature pattern analysis across the city of Rome, compared to analyses based solely on in situ measurements. Furthermore, the spatiotemporal pattern of the UHI, which can now be measured at high resolution, aligns well with the expected pattern.
How to cite: Cecilia, A., Casasanta, G., Petenko, I., Conte, M., Conidi, A., and Argentini, S.: High Spatiotemporal Resolution Determination of the Urban Heat Island in Rome Using Satellite LST Measurements and In Situ Air Temperature Data, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-128, https://doi.org/10.5194/egusphere-plinius18-128, 2024.
The extreme climatic events are increasing worldwide and are becoming more and more recurrent. It is becoming urgent to take action to reduce them. The Mediterranean region is one of the most affected areas by the effects of climate change. The recent events in France, Spain, Greece, Turkey or even Libya reflect the violence of hydro-meteorological phenomena due in part to the rising sea temperatures. To limit the occurrence of such events, it is necessary to have some feedback. PREDICT uses post-event spatial imaging techniques such as rapid mapping to obtains geospatial data in just a few hours after the event. This permits to manage and document the consequences of such events.
Furthermore, PREDICT has been developing a program called COSPARIN (COntribution du SPAtial à la gestion du Risque INondation) for few years now, with European Space Agency (ESA) and the National Centre of Spatial studies (CNES) approval. The main goal of this program is to better understand and monitor extreme events before, during and after their occurrence. This relies on an innovative method based on the use of the most recent satellite data available as well as algorithms and artificial intelligence to establish a global estimation of precipitations and an estimation of potential flooding areas.
To improve forecasting and deploy early warning systems, Europe has set up the HORIZON program which includes a new project called GOBEYOND (GeO and weather multi-hazard impact Based Early warning and response systems supporting rapid deploYment of first respONders in EU and beyond). The aim of this project is to improve existing tools and methods by designing two platforms of Multi Risk Impact-based Early Warning System (MR-IEWS) for Europe and Mediterranean area (North Africa included). This draws on all available data, including COSPARIN satellite data, to forecast hydro-meteorological risks (floods, storms, heatwaves) and geological risks (earthquakes, volcanic eruptions, tsunamis). Also, this aims to promote rapid and effective operational response by those in charge of safety and to move from simple hazard forecasting to risk forecasting by taking vulnerability into account.
Keywords: Climate change, Spatial data, Forecast, Innovation
How to cite: Guillaume, L. and Alix, R.: Spatial contribution to enhance early warning solutions for adapting to climate risks, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-16, https://doi.org/10.5194/egusphere-plinius18-16, 2024.
Accurate mapping and classification of non-perennial rivers (NPRs) is currently not available. However, in EU Member states, the implementation of the Water Framework Directive (WFD, 2000) requires continuous measurements or modeling of the natural flow rate in all water bodies. Recent studies have shown that the use of multispectral satellite imagery can effectively provide observations of flow or non-flow conditions with temporal resolution of about 5 days and limited to sections sufficiently wide (greater than around 30 m) and without vegetation cover. In addition, areas and periods with high cloud cover can lead to limitations on the availability of satellite imagery. On the other hand, the hydrological models require a large amount of data for the simulation of the water cycle in basins. The lack of gauging stations – typical condition for NPRs - for calibrating the models, and the fact that most of the models are constructed with reference to perennial rivers, make them unsuitable for simulating the hydrological regime of temporary waterbodies. Within the framework of the RIVERTEMP project [Erasmus+ 2022-1-IT02-KA220-HED-000086223], a methodology was created for combining hydrological modeling and satellite monitoring for determining the hydrological status of NPRs, using Keritis river basin (Chania, Greece) as a case study in the period 2019-2021. The analysis of the satellite images showed that in 69.5% of the observations the status of Keritis is “flowing” while in 30.5% is “ponding”. We used hydrological modeling to simulate river flows, then we calibrated the model by comparison with satellite observations and successively filled the date gaps of satellites. The comparison between satellite classification and modeled daily flowrate allowed the extraction of significant flow rate values, then used as threshold values to foresee the hydrological condition. This analysis showed that 59.6% of the results characterize the status of Keritis as “flowing”, 37% as “ponding” and 3.4% as "dry". The research results show that classified satellite data can be used to validate the prediction of hydrological models and, in turn, the results of hydrological model simulations can be used for the estimation of the hydrological conditions of NPRs when and where satellite images are not available.
How to cite: Nikolaidis, N., Lilli, M., Maragkaki, A., Cavallo, C., Manfreda, G., Papa, M. N., and Vezza, P.: Combining hydrological modeling and satellite observation to estimate the hydrological regime of non-perennial rivers, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-9, https://doi.org/10.5194/egusphere-plinius18-9, 2024.
Since early 2022, Italy has been experiencing a multi-year drought going well beyond a mere precipitation deficit to include a snow deficit, negative soil moisture anomalies, significant streamflow lows, and considerable impacts on various sectors. In the wake of this event, CIMA Research Foundation is working with the Italian Civil Protection Department to establish a real-time, multi-variable and operational water scarcity data platform for real-world applications. These analyses provide monthly snapshots of the following variables: temperature anomaly, Standardized Precipitation Index, Standardized Soil Moisture Index, Standardized Precipitation Evapotranspiration Index, Snow-Water-Equivalent deficit, and anomalies of the fraction of absorbed photosynthetically active radiation. All indices are computed at monthly resolution and are spatially explicit, with varying spatial resolutions up to a maximum of 1 km. In collaboration with the Italian Civil Protection Department, a bulletin is composed every month to translate these indices into decision-relevant information, such as the percentage of the Italian territory that is in a specific drought level every month. Future steps are the inclusion of near real-time, satellite-based information on water reservoir areas and extents for various case studies across the country.
How to cite: Avanzi, F., Cremonese, E., isabellon, M., Trotter, L., Pulvirenti, L., Cenci, L., Squicciarino, G., Maurer, T., Gabellani, S., Duro, A., Campione, E., Puca, S., Barbani, M., Gollini, A., Rossi, L., and Ferraris, L.: Towards an operational, multi-variable, and real-time water scarcity data platform for the Italian Civil Protection Department , 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-28, https://doi.org/10.5194/egusphere-plinius18-28, 2024.
El Caminito del Rey in Malaga (Spain) is a popular hiking trail known for its dramatic landscapes and towering cliffs. These cliffs, reaching up to 700 meters, are prone to rockfalls, with significant incidents reported in 2022 that damaged the pathway and blocked a segment of the exit path. This study focuses on identifying parts of the Caminito most at risk from these rockfalls and introduces a step-by-step method for assessing this hazard. Our approach begins with creating a detailed 3D digital model of the Caminito, which serves as the foundation for our susceptibility assessment. Next, we collect extensive data on the types of rocks that make up the cliffs—primarily limestone and conglomerate. This data is crucial because the rock type and structure may condition the probability of rock falls occurrence in the cliffs. With the 3D model and rock data, we then simulate potential rockfalls to see where they might impact the pathways. These simulations help us understand which areas are the most exposed to these phenomena. The results of simulations may help in reducing hazard supporting decisions on solutions to decrease the exposure of visitors to rockfalls. This contribution shares the results of our assessment in the Caminito del Rey and discusses how these findings could lead to better ways to prevent rockfalls and make the pathway safer. Our research offers a practical tool for the Caminito managers to better prepare for and prevent rockfalls.
How to cite: Galve, J. P., Sarro, R., Pérez-García, J. L., Ruiz-Fuentes, A., Gómez-López, J. M., Jerez-Longres, P. S., Martínez-Corbella, M., Riquelme, A., Mateos, R. M., and Azañón, J. M.: Rock fall susceptibility assessment at Caminito del Rey, Málaga, Spain, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-15, https://doi.org/10.5194/egusphere-plinius18-15, 2024.
The seasonal snowpack plays an important role for the water budget of the Mediterranean Sea, but an exact quantification of this contribution is still elusive. This is particularly true if one compares this preliminary understanding with previous work in other semi-arid regions of the world like the western US, where both the scientific community and importantly the water-resources management sector have already achieved consensus estimates on this matter, with snow supplying 30% to 80% of annual runoff. In order to provide such figures for the Mediterranean-Sea region, we developed a 6-year (2015-2021) reanalysis of Snow Water Equivalent (SWE) at approximately 1 km resolution and daily granularity for the whole basin of the Mediterranean Sea (Nile excluded). The reanalysis uses ERA5 meteorological data and satellite-based precipitation as input for a snow model, S3M, which then assimilates daily snapshots of snow depth from the C-SNOW Sentinel-1 product. These simulations were validated using in-situ snow depth measurements across the Mediterranean-Sea region. Maps of snow water equivalent from this reanalysis were spatially aggregated to provide a preliminary estimate of central tendencies and standard deviations of SWE for the Mediterranean Sea, as well as an estimate of peak-SWE and snowmelt timing. These estimates demonstrate the added value of remote-sensing products to tackle societally relevant questions in the 21st century.
How to cite: Avanzi, F., Lievens, H., Massari, C., Alfieri, L., Delogu, F., Campo, L., Libertino, A., Filippucci, P., Mosaffa, H., Quintana-Seguí, P., Gabellani, S., and De Lannoy, G.: How much snow is there across the Mediterranean region?, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-24, https://doi.org/10.5194/egusphere-plinius18-24, 2024.
A study of cut-off processing parameter effect on the representativeness of estimated GNSS tropospheric delay in occurrence of extreme weather events was performed. The analysis focuses on the impact due to GNSS observation geometry and its relationship to the type of event experienced. Data were collected by multi-constellation GNSS receivers located in the areas of interest referred to two peculiar case studies: Como Lake, affected by weather events of significant intensity and short duration (i.e. July 25th, 2021) and a sequence of events from Emilia Romagna-Veneto to Tuscany (i.e. August 18th, 2022). Characterisation of the timing and location of the event was performed using lightning from LINET network. Analyses highlight the impact of cut-off setting at different values and show how, in specific applications, an established cut-off value represents a fair trade-off between solution stability and representativeness of the studied event. Therefore, data assimilation into the Weather Research & Forecasting (WRF) Model of the estimated Zenith Total Delay (ZTD) from GNSS observations with cut-offs at 7 and 30 degrees was performed for both cases. Results show a consistent and substantial impact of the cut-off geometry on the WRF forecast at the short-term (0-6h).
How to cite: Mascitelli, A., Torcasio, R. C., Aruffo, E., Chiacchiaretta, P., Realini, E., Gatti, A., Laviola, S., Dietrich, S., Di Carlo, P., and Federico, S.: Effect of cut-off processing parameter on GNSS-ZTD representativeness in extreme weather events, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-25, https://doi.org/10.5194/egusphere-plinius18-25, 2024.
Medicanes originate from extra-tropical cyclones undergoing tropical-like cyclone transition during their mature phase, showing characteristics typical of tropical cyclones (TC): a barotropic structure with an axi-symmetric warm core originating from diabatic processes, strong rotation winds, and spiraling rain bands around a nearly cloud-free eye. In analogy with TCs, the surface wind field is useful to characterize Medicanes, as it could give additional information on their evolution. In particular, the radius of maximum wind (RMW) is a key feature for the TC’s intensification evaluation. Following the definition provided by the National Oceanic and Atmospheric Administration (NOAA) for the TCs, the RMW is defined as the distance between the band of the strongest winds and the Medicane’s center of rotation. However, an accurate RMW computation is sensitive to the methodology used for the determination of the Medicanes' center of rotation.
In this work we use the near-surface wind field provided by the Advanced SCATterometer (ASCAT) real-aperture radar onboard MetOp satellites. Moreover, to increase the temporal coverage, the Wind Radar (WindRAD) onboard of Feng Yun FY-3E satellite series is also used. For both sensors the surface wind field estimation is related to the roughness of the sea surface through the back-scattered electromagnetic signal. The scatterometer wind field as well as ERA-5 mean sea level pressure (MSLP) field are used as an indication of the Medicanes’ intensification.
The methodology developed to identify the center of rotation (which is often different from the position of the minimum MSLP) is based on the computation of the standard deviation of the horizontal surface wind direction also taking into account the wind speed field. The observations show that closer to the cyclone center the wind direction is highly variable due to the presence of the cyclonic vortex. This results in higher standard deviation’s values and can be considered a reliable feature to identify the cyclone's center. Moreover, since Mediterranean cyclones often exhibit satellite-based phenomenological features typical of TCs we also investigate the applicability of the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) algorithm, developed by the TC group at CIMSS/University of Wisconsin-Madison. ARCHER is widely used as an objective tool to locate the TC’s center of rotation.
The purpose of our work is to compare the two methodologies to better understand their potentialities and limitations in the characterization of the RMW and to relate the evolution of the RMW to the Medicanes' intensification. Several cases of Medicanes that have occurred in the last 10 years are analysed. The results show that both methodologies are more reliable when the Medicane is more organized showing a closed cyclonic structure associated with strong near-surface winds with a quasi-calm area in its center (mature phase), and that in most cases the RMW decreases as the Medicanes intensify. This study indicates that satellite-based monitoring of the RMW could provide useful indication for tracking Medicanes evolution in near-real time. This work is carried out within the ESA project “Earth Observations as a cornerstone to the understanding and prediction of tropical-like cyclone risk in the Mediterranean (MEDICANES)”.
How to cite: Sebastianelli, S., Panegrossi, G., D'Adderio, L. P., Sanò, P., Casella, D., and Herndon, D.: Two satellite-based methodologies for the automated detection of the center of rotation in Mediterranean tropical-like cyclones, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-40, https://doi.org/10.5194/egusphere-plinius18-40, 2024.
Among the data made available by the European Copernicus Sentinel missions, those acquired by sensors aboard Sentinels 1 and 2 satellites have already proven to be suitable for detecting and monitoring floods, with different capabilities depending on the characteristics of the instrument used. C-band SAR aboard Sentinel 1A and 1B can provide all-day and all-weather information with a spatial resolution of 10 m a sub-weekly temporal resolution (when both satellites are working together), whereas the MultiSpectral Instrument on Sentinels 2A and 2B can provide data only in daytime conditions and in the absence of clouds with a slightly lower spatial resolution (i.e., 20 m) and almost a similar temporal resolution. Nevertheless, their integration may allow for a more accurate and comprehensive investigation of the studied event if adequate data analysis methodologies are used. This study presents a multitemporal approach to map flooded areas using long-term historical series of Sentinel 1 and Sentinel 2 data. Such an approach, based on a preliminary characterization of the expected value of the investigated signal at the pixel level, can allow for robust identification of any signal transients related to the occurrence of statistically significant change within the pixel, such as a different and/or increased water presence due to floods. The Google Earth Engine (GEE) cloud computing system, where all historical data are present and accessible, was used to implement the proposed methodology. In addition to Sentinel 1 and 2 data, all other datasets/tools useful for developing the proposed methodology are already available in GEE, facilitating its implementation and application to the analysis of different flood episodes. The results achieved for a few events that occurred worldwide using each single approach and their integration were compared with flood maps made available by the Copernicus Emergency Monitoring Service system to assess their accuracy. The performance achieved is discussed in this study.
How to cite: Lacava, T., Albano, R., Lahsaini, M., and Mazzariello, A.: On a multitemporal analysis of Copernicus Sentinel data for a robust and near-real time mapping of floods, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-75, https://doi.org/10.5194/egusphere-plinius18-75, 2024.
Simulation models are used to calculate the tsunami wave propagation and ultimately the tsunami height at shoreline and run-up. In recent years, several researchers are working on improving the output of these simulations. Although it could be characterized as accurate, both the difficulty of collecting data and calculating sub-scenarios, the high number of factors controlling tsunami properties, and the fact that the output will only be for a very specific tsunami scenario, make this process difficult and time-consuming. Furthermore, a difficulty has been noted in disseminating the related information to the general public and its final acceptance by Civil Protection agencies for further implementation of relevant risk reduction measures as the results refer to the occurrence of a rare scenario without detailed indication of the possible impacts on individual coastal areas.
In the context of this research, a new methodology for calculating coastal impacts from tsunami is presented. It groups a large number of scenarios of tsunami generation and evolution according to the final rup-up and aims to highlight the possible impacts on the coastal zone according to the tsunami intensity. It is based on the compilation of a tsunami inventory comprising historical and recent events that have affected the study area, without taking into account a single tsunami case. The proposed methodology results in the tsunami inundation zoning of the studied coastal area based on already generated events.
Subsequently, using satellite imagery, a highly detailed analysis of significant buildings and critical infrastructure located within the inundated coastal zone is carried out along with a classification of the potential impacts into 5 main categories comprising: (i) moving objects, (ii) infrastructure, (iii) buildings, (iv) natural environment and (v) population, a classification proposed within the Integrated Tsunami Intensity Scale ITIS2012.
This methodology can contribute to the adoption of measures to effectively mitigate tsunami impacts on the built environment, resilience and sustainability of coastal infrastructure, to the development of safer spatial and urban planning and to a precise estimation of economic losses from coastal inundation. In addition, it is an important tool for operational planning, in particular for the selection and operation of emergency sites in the coastal zone, the preparation of tsunami contingency plans, and the implementation of actions to increase the operational preparedness of Civil Protection agencies and the awareness of the general population and its special groups (students, volunteers, elderly and disabled people, etc.) about tsunamis and their impacts.
How to cite: Gogou, M., Mavroulis, S., Evelpidou, N., Stagonas, D., and Lekkas, E.: Effective estimation of Tsunami Coastal Impacts based on Tsunami Inventories, Satellite Imagery and Inundation Zoning, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-102, https://doi.org/10.5194/egusphere-plinius18-102, 2024.
Mediterranean Tropical-Like Cyclones or “Medicanes” tracking is a crucial aspect of understanding and predicting these rare meteorological phenomena. In this study, we aimed to evaluate and compare two tracking methods based on the genesis index (GEN) and mean sea level pressure. The GEN index, initially developed for tropical cyclones (Emanuel and Nolan, 2004), integrates various meteorological parameters, including the idealized maximum wind speed or Potential Intensity (PI), low-tropospheric vorticity, mid-tropospheric relative humidity, and deep-layer wind shear. While this index has shown promises in the literature, for tropical cyclones (Emanuel and Nolan, 2004), but also in synthetic generation of medicane tracks for climatological studies over the Mediterranean (Romero and Emanuel, 2013), its performance in tracking tropical-like cyclones and its comparison to the the most common tracking method remains uncertain. Our analysis is carried out on a subset of seven documented medicane cases from 2014 to 2021, focusing on those exhibiting warm-core characteristics in the satellite observations (Panegrossi et al 2023). Using ERA5 reanalysis data, we tracked these medicanes with two methods, one based on the tracking of minimum mean sea level pressure, and one based on the tracking of maximum GEN index values and compared the results against the reference track as obtained by combination of different tracking methods (Flaounas et al., 2023). The information about the warm core center provided by passive microwave radiometers is used as reference. Our findings reveal that while the GEN index tracking method presents greater RMSE than the mean sea level pressure tracking method compared to the reference, it demonstrates improved performance in capturing intense phases of cyclones, particularly when these cyclones exhibit deep warm core characteristics for ERA5 as evidenced by means of the ERA5-based Hart Parameters (Hart, 2003). We observed that initial phases of cyclone development pose greater challenges for both tracking methods, suggesting higher uncertainty in ERA5 reanalysis center location during early cyclogenesis stages. Our study provides valuable insights into medicane tracking methodologies and highlights the need for continued refinement and validation using satellite observations, particularly in the context of ERA5 reanalysis data. Further research efforts are warranted to optimize tracking methods, especially in the early development phase, and improve our understanding of medicane dynamics, ultimately enhancing forecast accuracy and preparedness for these impactful weather events in the Mediterranean region.
References:
Emanuel K.A. and D. S Nolan. Tropical cyclone activity and the global climate system. In 26th Conf. on Hurricanes and Tropical Meteorology, pages 240–241, 2004.
Romero R. and K.A. Emanuel. Medicane risk in a changing climate. Journal of Geophysical Research: Atmospheres, 118(12):5992–6001, 2013.
Panegrossi G., at el.. Warm core and deep convection in medicanes: A passive microwave-based investigation. Remote Sensing, 15(11):2838, 2023.
HART, R. E. A cyclone phase space derived from thermal wind and thermal asymmetry. Monthly weather review, 131.4: 585-616,2003.
Flaounas E. et al., A composite approach to produce reference datasets for extratropical cyclone tracks: application to Mediterranean cyclones. Weather and Climate Dynamics, 4, 639-661, 2023.
How to cite: Saraceni, M., D'Adderio, L. P., Silvestri, L., Panegrossi, G., and Bongioannini Cerlini, P.: Evaluation of Tracking Methods for Mediterranean Tropical-Like Cyclones using satellite observations: A Comparative Study Using the Generation Index and Mean Sea Level Pressure, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-140, https://doi.org/10.5194/egusphere-plinius18-140, 2024.
