Session PL2 

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.

The interaction between aerosol particles and thunderstorm evolution and properties is complex and was studied by direct observational campaigns, remote sensing from space and through numerical simulations. Aerosols invigorate convection and can lead to enhanced charging manifested in more lightning, but they can also lead to a "Boomerang Effect" where too large concentrations of particles lead to diminished vertical development and weaker electrical activity (Altaratz et al., 2010). The effects of ship exhaust on ocean cloudiness have been studied intensively in recent years, following the discovery of prolonged ship tracks in oceanic regions where maritime transportation is most heavy, leading to large-scale changes in albedo and reduced precipitation. Recently it was shown that aerosols emitted by ships also tend to increase lightning activity, by modifying the dynamics and microphysics of clouds formed close to the busiest shipping lanes (Thornton et al., 2017) and enhancing the strike probability due to the tall metal ship structure (Peterson, 2023).

We study the effects of ship-emitted aerosols on thunderstorms in one of the busiest shipping routes in the world: the Mediterranean Sea between the Suez Canal and the Gibraltar Straights (see: https://www.marinetraffic.com/). This region hosts hundreds of ships daily, and space observations show considerable enhancement of the Aerosol Optical Depth (AOD), Sox and NOx concentrations there, some from land sources and others directly related to ship emissions. The present study utilized lightning detection networks' data (ENTLN) and researched the properties of lightning (peak current, multiplicity, polarity) with respect to aerosol concentrations and meteorological conditions. The shipping exhaust data was derived from the CAMS global emission inventories.

Initial results from the Eastern Mediterranean shows a marked increase in winter (DJF) lightning activity over the main east-west shipping lanes from Suez towards Crete, where a conspicuously larger amount of cloud-to-ground lightning is observed, with a higher fraction of superbolts (I > 200 kA). We suggest that the synergistic action of desert dust and air-pollution aerosols acts to invigorate or diminish convection, depending on their relative concentrations and the ambient meteorological conditions. This changes the effectiveness of charge separation processes and affects the electrical activity of winter thunderstorms in these specific areas of the Mediterranean Sea.

Altaratz, O., I. Koren, Y. Yair, and C. Price (2010), Lightning response to smoke from Amazonian fires, Geophys. Res. Lett., 37, L07801, doi:10.1029/2010GL042679

Peterson, M. (2023). Interactions between lightning and ship traffic. Earth and Space Science, 10(11). https://doi.org/10.1029/2023EA002926

Thornton, J. A., Virts, K. S., Holzworth, R. H., & Mitchell, T. P. (2017). Lightning enhancement over major oceanic shipping lanes. Geophysical Research Letters, 44(17), 9102–9111. https://doi.org/10.1002/2017GL074982

How to cite: Yair, Y., Korzets, M., Namia-Cohen, Y., Price, C., and Surian, N.: Lightning superbolts follow ship-tracks in Eastern Mediterranean winter thunderstorms, 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 Sep–3 Oct 2024, Plinius18-5, https://doi.org/10.5194/egusphere-plinius18-5, 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 (T_a) 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.

T_a 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 T_a data available for the city of Rome (Italy) during the summers of 2019 and 2020. The T_a 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, T_a was estimated, designated as the target variable, at points where no in situ measurement sensors are available. The T_a 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.