Search Results (44)

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

Storm Daniel is arguably one of the most severe Mediterranean tropical-like cyclones (medicanes) ever recorded. Greece was one of the most affected areas, especially the central part of the country. The extreme precipitation that was observed along with the subsequent extensive flooding was considered a critical challenge to validate the regional version of the ICON (Icosahedral Non-Hydrostatic) numerical weather prediction (NWP) model. From a methodological standpoint, the short-range nature of the model was realized with 48 h runs over a sequence of cases that covered the storm period. The development of the medicane was highlighted via the tracking of the minimum mean sea level pressure (MSLP) in reference to the corresponding analysis of the European Center for Medium-Range Weather Forecasts (ECMWF). In a similar fashion, snapshots regarding the 500 hPa geopotential associated with the 850 hPa temperature were addressed at the 24th forecast hour of the model runs. Although the model’s performance over the four most affected synoptic stations of the Hellenic National Meteorological Service (HNMS) was mixed, the overall accumulated forecasted precipitation was in very good agreement with the corresponding total value of the observations over all the available synoptic stations. Full article

The precise estimation of precipitation is key to understanding and mitigating the effects of extreme weather conditions, especially in areas susceptible to Mediterranean cyclones. This work assesses the performance of the integrated multi-satellite retrievals for GPM (IMERG) precipitation products during the extreme Mediterranean cyclone “Medicane Daniel” that affected the Thessaly Plain in Central Greece in early September 2023. Three IMERG versions (final run (FR), early run (ER), and late run (LR)) were inter-compared with gauge-based interpolated rainfall estimates using inverse distance weighting (IDW) and ordinary kriging techniques. Pixel-wise and categorical verification metrics, such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS), were calculated for rainfall thresholds between 50 mm and 400 mm. It was found that the IMERG final run agreed most with the ground observations, with a correlation coefficient (R) of 0.87, RMSE of 138.8 mm, and CSI up to 0.995 at the 100 mm threshold when the IDW interpolation was used. Kriging produced slightly better spatial accuracy overall, as indicated by a lower RMSE (14.5 mm) and higher correlation (R = 0.99). The results indicate the benefit of combining satellite precipitation data with ground-based observations through spatial interpolation for the enhanced monitoring of extreme weather events over complex terrain. Kriging is suggested when greater spatial reliability is needed, while IMERG-FR is found to be a reliable satellite product for quick response analysis during heavy precipitation events. The study emphasizes the importance of blending satellite precipitation estimates and ground observations via spatial interpolation methods, i.e., kriging and IDW, allowing for a more localized and precise validation of intense weather events. Full article

This study examines the evolution, structure, and dynamic and thermodynamic mechanisms of a Mediterranean tropical-like cyclone (TLC), or medicane (from Mediterranean–Hurricane), that occurred in the central Mediterranean region from 15 to 19 September 2020. This event is considered an extreme meteorological phenomenon, particularly impacting the Greek area and affecting the country’s economic and social structures. It is one of the most significant recorded Mediterranean cyclone phenomena in the broader Mediterranean region. The synoptic and dynamic environment, as well as the thermodynamic structure of this atmospheric disturbance, were analyzed using thermodynamic parameters. The system’s development can be described through three distinct phases, characterized by its symmetrical structure and warm core, as illustrated in the phase space diagrams and further supported by dynamical analysis. During the first phase, on 15 September, the structure of the upper tropospheric layers began to strengthen the parent barometric low, which had been in the Sirte Bay region since 13 September. The influence of upper-level dynamical processes was responsible for the reconstruction of the weakened barometric low. In the second phase, during the formation of the Mediterranean cyclone, low-level diabatic processes determined the evolution of the surface cyclone without significant support from upper-tropospheric baroclinic processes. Therefore, in this phase, the system is characterized as barotropic. In the third phase, the system remained barotropic but showed a continuous weakening tendency as the sea surface pressure steadily increased. This comprehensive analysis highlights the intricate processes involved in the development and evolution of Mediterranean cyclones with tropical characteristics. Full article

This paper presents a detailed analysis of the energy dynamics of the Mediterranean tropical-like cyclone, Medicane Ianos, by using a moist static energy (MSE) budget framework. Medicanes are hybrid cyclonic systems that share characteristics of both extratropical and tropical cyclones, making their classification and prediction challenging. Using high-resolution ERA5 reanalysis data, we analyzed the life cycle of Ianos, which is one of the strongest recorded medicanes, employing the vertically integrated MSE spatial variance budget to quantify the contributions of different energy sources to the cyclone’s development. The chosen study area was approximately 2500² km², covering the entire track of the cyclone. The budget was calculated after tracking Ianos and applying Hart phase space analysis to assess the cyclone phases. The results show that the MSE budget can reveal that the cyclone development was driven by a delicate balance between convection and dynamical factors. The interplay between vertical and horizontal advection, in particular the upward transport of moist air and the lateral inflow of warm, moist air and cold, dry air, was a key mechanism driving the evolution of Ianos, followed by surface fluxes and radiative feedback. By analyzing what process contributes most to the increase in MSE variance, we concluded that Ianos can be assimilated in the tropical framework within a radius of 600 km around the cyclone center, but only during its intense phase. In this way, the budget can contribute as a diagnostic tool to the ongoing debate regarding medicanes classification. Full article

The Thessaly Plain, Greece’s largest alluvial basin, has undergone significant geological, hydrological, and anthropogenic transformations. This study synthesises historical records, geological and hydrogeological studies to assess the evolution of the East Thessaly Plain, focusing on land use changes, groundwater management, and environmental challenges. Intensive agricultural practices, particularly from the 1970s onward, have led to groundwater overexploitation, land subsidence, and declining water quality. The overexploitation of the aquifers, exacerbated by extensive irrigation and inefficient water management, has resulted in critical groundwater shortages and widespread subsidence, particularly in the Larissa–Karla and Titarisios Cone systems. Additionally, recent extreme weather events, including Medicane Daniel (2023) and Medicane Ianos (2020), have highlighted the region’s vulnerability to hydrological hazards, with extensive flooding affecting urban and agricultural areas. The re-emergence of Lake Karla as a flood retention area underscores the unintended consequences of past drainage efforts. Remote sensing, geodetic surveys, and historical records have been examined to assess the interplay between groundwater withdrawals, land subsidence, and flood risks. Full article

Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In response to this issue, we present an AI-based system for tracking and forecasting Medicanes, employing machine learning techniques to identify cyclone positions and key evolving spatio-temporal structural features of the cloud system that are associated with their intensification and potential extreme development. While the forecasting model currently operates with limited training data, it can predict extreme Medicane events up to two days in advance, with precision rates ranging from 65% to 80%. These innovative data-driven methods for tracking and forecasting provide a foundation for refining AI models and enhancing our ability to respond effectively to such events. Full article

Mediterranean tropical-like cyclones, or Medicanes, present unique challenges for precipitation estimations due to their rapid development and localized impacts. This study evaluates the performance of satellite precipitation products in capturing the precipitation associated with Medicane Daniel that struck Greece in early September 2023. Utilizing a combination of ground-based observations, reanalysis, and satellite-derived precipitation data, we assess the accuracy and spatial distribution of the satellite precipitation products GPM IMERG, GSMaP, and CMOPRH during the cyclone event, which formed in the Eastern Mediterranean from 4 to 7 September 2023, hitting with unprecedented, enormous amounts of rainfall, especially in the region of Thessaly in central Greece. The results indicate that, while satellite precipitation products demonstrate overall skill in capturing the broad-scale precipitation patterns associated with Medicane Daniel, discrepancies exist in estimating localized intense rainfall rates, particularly in convective cells within the cyclone’s core. Indeed, most of the satellite precipitation products studied in this work showed a misplacement of the highest amounts of associated rainfall, a significant underestimation of the event, and large unbiased root mean square error in the areas of heavy precipitation. The total precipitation field from IMERG Late Run and CMORPH showed the smallest bias (but significant) and good temporal correlation against rain gauges and ERA5-Land reanalysis data as a reference, while IMERG Final Run and GSMaP showed the largest underestimation and overestimation, respectively. Further investigation is needed to improve the representation of extreme precipitation events associated with tropical-like cyclones in satellite precipitation products. Full article

Mediterranean hurricanes, also known as medicanes, can cause significant damage to coastal communities. Consequently, they have been the subject of intense study in recent decades. Geographic Information Systems (GISs) and Web-GIS applications are essential for processing and analyzing geographic data from various sources, particularly in the field of spatial planning. Due to their ability to integrate heterogeneous information, these tools can provide experts with a comprehensive overview of relevant geospatial data. Here, we present ARCHIMEDE, a relational geodatabase connected to an open-source Web-GIS platform focused on Mediterranean hurricanes that contains data from recent research on these extreme weather phenomena. These datasets comprise climatic and oceanographic data obtained from remote sensing techniques as well as seismic and geomorphological data obtained from field observations. Consequently, this Web-GIS platform can enhance our understanding of Mediterranean cyclones by relating the physical properties of these natural phenomena with their impacts on coastal regions. ARCHIMEDE is an innovative tool for the dissemination of geographic information to stakeholders, researchers, and decision-makers, offering valuable support for the development of robust coastal management strategies aimed at mitigating the challenges posed by Mediterranean hurricanes. Full article

Morphological features of the Mediterranean Sea basin have recently been precursors to a significant increase in the formation of extreme events, in relation to climate change effects. It happens very frequently that rotating air masses and the formation of mesoscale vortices can evolve into events with characteristics similar to large-scale tropical cyclones. Generally, they are less intense, with smaller size and duration; thus, they are called Medicanes, a short name for Mediterranean hurricanes, or tropical-like cyclones (TLCs). In this paper, we propose a new perspective for the study and analysis of cyclonic events, starting with data and images acquired from satellites and focusing on the diagnostics of the evolution of atmospheric parameters for these events. More precisely, satellite remote sensing techniques are employed to elaborate on different high spatial-resolution satellite images of the events at a given sensing time. Two case studies are examined, taking into account their development into Medicane stages: Ianos, which intensified in the Ionian Sea and reached the coast of Greece between 14 and 21 September 2020, and Apollo, which impacted Mediterranean latitudes with a long tracking from 24 October to 2 November 2021. For these events, 20 images were acquired from two different satellite sensors, onboard two low-Earth orbit (LEO) platforms, by deeply exploiting their thermal infrared (TIR) spectral channels. A useful extraction of significant physical information was carried out from every image, highlighting several atmospheric quantities, including temperature and altitude layers from the top of the cloud, vertical temperature gradient, atmospheric pressure field, and deep convection cloud. The diagnostics of the two events were investigated through the spatial scale capabilities of the instruments and the spatiotemporal evolution of the cyclones, including the comparison between satellite data and recording data from the BOLAM forecasting model. In addition, 384 images were extracted from the geostationary (GEO) satellite platform for the investigation of the events’ one-day structure intensification, by implementing time as the third dimension. Full article

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. Full article

Ianos originated from a shallow low in Libya, which formed on the tip of a warm tongue of Saharan air on 12 September 2020, a couple of days before its transformation to a Medicane. The genesis of the incipient cyclone was associated with strong warm air advection, across the Saharan low-level baroclinic zone, ahead of an amplifying thermal wave. On 13 September, when the baroclinic zone weakened, the upper levels played a significant role in the maintenance of the low. The relative position of the upper-level jet favoured downward cyclonic vorticity advection towards the cyclone, which apparently acted to reinforce the cyclonic circulation until the low entered the sea on 14 September 2020. Full article

Sea surface chlorophyll concentrations are indicative of phytoplankton growth and can be impacted by extreme weather events. Hurricanes and typhoons have been widely studied for such an influence on the marine environment; chlorophyll increases and even phytoplankton blooms have been reported. In this study, a tropical-like Mediterranean cyclone, the medicane Ianos of September 2020, that affected a large oligotrophic area of the Ionian Sea, is examined from this perspective. A numerical model and satellite data were used for delineating the study area and assessing chlorophyll variations, respectively. On a smaller geographical scale in respect to tropical cyclones, the medicane-triggered chlorophyll increases comparable to those of hurricanes when affecting oligotrophic open sea waters. Full article

Data assimilation is a technique used to combine observational data with numerical weather analysis fields to produce input conditions for Numerical Weather Prediction (NWP) models aimed at more accurate forecasts. In this study, two different regional Background Error (BE) covariance statistics are evaluated for the implementation of a Weather Research and Forecast (WRF) model using Variational Data Assimilation (VAR) schemes. Two different WRF model forecasts, initialized at different times, are compared to calculate different regional BE statistics based on the National Meteorological Center (NMC) technique. These statistics are then used in the three-dimensional variational (3DVAR) data assimilation process to produce analysis fields for a 15 day period during September 2019. The study compares the forecasts produced using the two different ΒΕ statistics and presents the results of the analysis fields during the medicane “Ianos” formed in September 2020 in the eastern Mediterranean Sea. The study demonstrates the importance of BE statistics’ usage in data assimilation. The results suggest that different initialization times can lead to significant differences in weather evolution. The study also highlights the need for caution in the choice of BE statistics and the importance of best practices in data assimilation. Full article

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. Full article