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The study of atmospheric models and climate systems has been hampered by the restricted availability of wind profile measurements. By recording wind profiles in near real time and giving useful information for Numerical Weather Prediction (NWP) models, the Doppler wind lidar technology used in the European Space Agency's Aeolus mission is expected to unravel this problem. By analyzing the precipitation from IANOS medicane, which occurred over the Thessaly plain in September 2020, a case study utilizing the COSMO NWP model illustrates the potential usefulness of Aeolus data in strengthening NWP models. Run in hindcast mode and forced by analyses with and without Aeolus, the model assimilated data that were produced at the European Centre for Medium-Range Forecasts (ECMWF). Evaluation against observations from Greek synoptic stations showed that the model precipitation using the Aeolus-assimilated data preponderated over the model results without their inclusion. This work aims to demonstrate the additional value of the Aeolus project towards the expansion of our knowledge of Earth's atmosphere, particularly the improvement of our capacity to estimate severe weather events via the use of Aeolus with NWP models. [ABSTRACT FROM AUTHOR]

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane's intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes' WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes' definition. [ABSTRACT FROM AUTHOR]

The rotating-convection paradigm for tropical cyclone behaviour is shown to provide an attractive and consistent framework for interpreting the dynamics of formation and intensification of at least some medicanes. The ideas are illustrated by a case study of the medicane that formed over the eastern Mediterranean in mid-December 2020. This case study is based on analyses of data from the European Centre for Medium Range Weather Forecasts (ECMWF), imagery from the European geostationary meteorological satellite, Meteosat Second Generation, and output from a convection permitting numerical simulation of the event using the United Kingdom (UK) Met Office regional model with the RAL2 physics configuration. Limitations of the currently widely accepted interpretation of medicanes in terms of the so-called Wind-Induced Surface Heat Exchange (WISHE) intensification mechanism are discussed.

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Comprehensive Nuclear-Test-Ban Treaty. We use data from the infrasound station IS48 of the International Monitoring System in Tunisia to investigate the infrasound signatures of mesocyclones using a multi-channel correlation algorithm. We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High- and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0.1 and 0.5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations. Multi-source situations are highlighted, stressing the need for more resilient detection-estimation algorithms. Cloud-to-ground lightning seems not to explain all high-frequency detections, suggesting that additional sources of electrical or dynamical origin may be at play that are related to deep convective systems. [ABSTRACT FROM AUTHOR]

Medicanes, a type of strong hurricanes/cyclones occurring in the Mediterranean, can be the source of major geohazard events in Mediterranean coastal and inland areas. Medicane Ianos that hit Greece during 17–19 September 2020 caused widespread damage, with numerous landsides and floods being the most prominent. Following the landfall of Medicane Ianos, a series of field surveys were launched together with rapid response through satellite imagery. We focused on two of the areas most affected by Medicane Ianos, Cephalonia island and Karditsa, Thessaly, both in Greece. A rapid landslide inventory for the Karditsa region was prepared using Copernicus Sentinel-2 satellite imagery, the first of its kind for a severe weather event in Greece. The mountainous area of Karditsa region in western Thessaly experienced the unprecedented number of 1696 landslides, mapped through satellite imagery and examined in the field. Cephalonia Island experienced a smaller number of landsides but damaging debris flows and severe structural damages. The rapid landside inventory was then compared to new methods of automated landslide mapping through change detection of satellite imagery. [ABSTRACT FROM AUTHOR]

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields. [ABSTRACT FROM AUTHOR]

The limited availability of wind profile measurements has hindered atmospheric models and climate systems’ understanding. The European Space Agency’s Aeolus mission’s space-based Doppler wind lidar technology could solve this issue by measuring wind profiles in Near-Real-Time, providing valuable data for Numerical Weather Prediction (NWP) models. A case study using the COSMO NWP model demonstrates the potential of Aeolus data in improving NWP models by examining the impact of Medicane IANOS in September 2020 over the Thessaly plain. The study aims to improve our ability to predict severe weather events and advance our understanding of Earth’s atmosphere.

A forward advection scheme is incorporated in an advanced data assimilation model to provide very short-term predictions. The Local Analysis and Prediction System (LAPS) is implemented in the nowcasting mode in a case study of extreme precipitation event over Attica, Greece. The LAPS assimilated remote sensing data from satellite retrievals and XPOL radar precipitation measurements to produce objective analyses alongside their nowcasts in a forecast window up to 3 h. The results indicate that the assimilation of remote sensing data can increase the short-term precipitation predictability, with varying performance depending on the type and the combination of the assimilated remote sensing data.

The rotating-convection paradigm for tropical cyclone behaviour is shown to provide an attractive and consistent framework for interpreting the dynamics of formation and intensification of at least some medicanes. The ideas are illustrated by a case study of the medicane that formed over the eastern Mediterranean in mid-December 2020. This case study is based on analyses of data from the European Centre for Medium Range Weather Forecasts (ECMWF), imagery from the European geostationary meteorological satellite, Meteosat Second Generation, and output from a convection permitting numerical simulation of the event using the United Kingdom (UK) Met Office regional model with the RAL2 physics configuration. Limitations of the currently widely accepted interpretation of medicanes in terms of the so-called Wind-Induced Surface Heat Exchange (WISHE) intensification mechanism are discussed. [ABSTRACT FROM AUTHOR]

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane’s intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes’ WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes’ definition.

Accurate modelling of air-sea processes is essential for reliable forecasts of Mediterranean tropical-like cyclones (also known as "Medicanes"). Medicanes occasionally develop in the Mediterranean causing extreme weather conditions with catastrophic potential due to excessive precipitation, windstorms, and coastal flooding. In this work, we investigate how the complexity of ocean-wave-atmosphere coupling and model initialization affect the simulated track and intensity of the Medicane Ianos (2020). Results indicate that the model's initial conditions and the cyclone's development stage are the main drivers of track position errors, while ocean and wave feedback have a significant impact on the intensity and evolution of the cyclone. Compared with an atmosphere-only simulation, an atmosphere-ocean coupled system reproduces the cyclone's SST cooling effect (up to 3.7 °C), in agreement also with the satellite observations thus, reducing the cyclone intensity, as estimated by the minimum MSLP, the 10-m wind speed and the surface enthalpy flux. Adding a wave model to the coupled system, further increases the magnitude of ocean cooling (by about 1.2 °C), due to increased sea surface roughness leading to increased wind stress and enhanced upper ocean mixing. Overall, surface waves are shown to have competing effects on cyclone intensity i.e., negative feedback via increasing the surface momentum flux and positive feedback via increasing the enthalpy flux, the latter being more sensitive to surface roughness rather than to SST modifications brought by the wave coupled system. The turbulent air-sea fluxes under high winds, appear to be very sensitive to sea-state patterns resolved by the coupled models, highlighting the need to improve forecasting systems for extreme weather events in the Mediterranean. • Coupled ocean–wave-atmosphere model have been developed to study the Medicane Ianos. • Both wave-current and wave-atmosphere interaction processes are considered. • Coupled model reproduces the upper-ocean cooling, reducing Medicane intensity. • Medicane track forecasts are strongly depend on the cyclone's development stage. • Wave effects on sea surface roughness must be considered in Medicane forecasting. [ABSTRACT FROM AUTHOR]

On 18 September 2020, the Karditsa prefecture of Thessaly region (Greece) experienced a catastrophic flood as a consequence of the IANOS hurricane. This intense phenomenon was characterized by rainfall records ranging from 220 mm up to 530 mm, in a time interval of 15 h. Extended public infrastructure was damaged and thousands of houses and commercial properties were flooded, while four casualties were recorded. The aim of this study was to provide forensic research on a reconstruction of the flood event in the vicinity of Karditsa city. First, we performed a statistical analysis of the rainfall. Then, we used two numerical models and observed data, either captured by satellites or mined from social media, in order to simulate the event a posteriori. Specifically, a rainfall–runoff CN-unit hydrograph model was combined with a hydrodynamic model based on 2D-shallow water equations model, through the coupling of the hydrological software HEC-HMS with the hydrodynamic software HEC-RAS. Regarding the observed data, the limited available gauged records led us to use a wide spectrum of remote sensing datasets associated with rainfall, such as NASA GPM–IMREG, and numerous videos posted on social media, such as Facebook, in order to validate the extent of the flood. The overall assessment proved that the exceedance probability of the IANOS flooding event ranged from 1:400 years in the low-lying catchments, to 1:1000 years in the upstream mountainous catchments. Moreover, a good performance for the simulated flooding extent was achieved using the numerical models and by comparing their output with the remote sensing footage provided by SENTINEL satellites images, along with the georeferenced videos posted on social media. [ABSTRACT FROM AUTHOR]

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Comprehensive Nuclear-Test-Ban Treaty. We use data from the infrasound station IS48 of the International Monitoring System in Tunisia to investigate the infrasound signatures of mesocyclones using a multi-channel correlation algorithm. We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High- and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0.1 and 0.5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations. Multi-source situations are highlighted, stressing the need for more resilient detection-estimation algorithms. Cloud-to-ground lightning seems not to explain all high-frequency detections, suggesting that additional sources of electrical or dynamical origin may be at play that are related to deep convective systems.

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields.

The present work analyzes the synoptic, thermodynamic, and microphysics characteristics of two Mediterranean cyclones that occurred in February–March 2023. The analysis is mainly carried out through the use of passive microwave (PMW) satellite measurements, which allow us to follow the cyclones' evolution and state whether Helios and Juliette can be considered as Mediterranean tropical-like cyclones (i.e., medicanes). Both cyclones show a very similar evolution, with a low-stratospheric warm air anomaly during the development phase, followed by the development of a warm anomaly in the low−/mid-troposphere. This feature is often observed in medicanes (e.g., Qendresa, Zorbas), except for few cases (i.e., Medicane Ianos, which shows a warm core (WC) development clearly driven by diabatic processes without a preliminary warming signal in the lower stratosphere and upper troposphere). The analysis carried out highlights that, while Helios maintains this setting through its whole lifetime, Juliette undergoes tropical transition in the final stage of its evolution. As opposed to most medicane cases, the PMW precipitation microphysics diagnostics shows the predominance of shallow clouds, with almost total absence of ice hydrometeors and deep convection in the proximity of the WC center (i.e., within 100 km radius) for Helios and during the initial stage of Juliette. PMW radiometry provides strong indication that diabatic heating plays a role in the WC development when the onset of deep convection features is identified in the proximity of the Juliette cyclone center. Moreover, the PMW cloud-top height product does not show a closed cloud-free eye for Helios, while it is observed for the final stage of Juliette as often happens during medicanes' mature phase. Therefore, we deem that while Helios can be labeled as a warm seclusion, Juliette can be included in the tropical-like cyclone category. • Helios and Juliette originate from similar thermodynamic and synoptic conditions. • A warm core originating from diabatic processes develops in Juliette at a later stage. • Deep convection features close to the center are never found in Helios. • Juliette shows deep convection within the warm core region. • Helios is a warm seclusion; Juliette is classified as a tropical-like cyclone. [ABSTRACT FROM AUTHOR]

On 18 September 2020, the Karditsa prefecture of Thessaly region (Greece) experienced a catastrophic flood as a consequence of the IANOS hurricane. This intense phenomenon was characterized by rainfall records ranging from 220 mm up to 530 mm, in a time interval of 15 h. Extended public infrastructure was damaged and thousands of houses and commercial properties were flooded, while four casualties were recorded. The aim of this study was to provide forensic research on a reconstruction of the flood event in the vicinity of Karditsa city. First, we performed a statistical analysis of the rainfall. Then, we used two numerical models and observed data, either captured by satellites or mined from social media, in order to simulate the event a posteriori. Specifically, a rainfall–runoff CN-unit hydrograph model was combined with a hydrodynamic model based on 2D-shallow water equations model, through the coupling of the hydrological software HEC-HMS with the hydrodynamic software HEC-RAS. Regarding the observed data, the limited available gauged records led us to use a wide spectrum of remote sensing datasets associated with rainfall, such as NASA GPM–IMREG, and numerous videos posted on social media, such as Facebook, in order to validate the extent of the flood. The overall assessment proved that the exceedance probability of the IANOS flooding event ranged from 1:400 years in the low-lying catchments, to 1:1000 years in the upstream mountainous catchments. Moreover, a good performance for the simulated flooding extent was achieved using the numerical models and by comparing their output with the remote sensing footage provided by SENTINEL satellites images, along with the georeferenced videos posted on social media.