This study investigates the dynamics of the development phases of a Mediterranean tropical-like cyclone (medicane) in the southern Ionian Sea, on 28 September 2018 that caused high impact phenomena in the central and eastern Mediterranean, focusing on the transition from explosive cyclone to medicane. The symmetry and the warm core structure of the system have been demonstrated via phase space diagrams determining three phases of the system development that are then supported on a dynamical basis. During the first phase of the system, baroclinic instability triggered the formation of the explosive cyclone, when strong upper-level PV anomalies at the dynamic tropopause level moved towards a pre-existed area of enhanced low-level baroclinicity over the coastal areas of Libya along with positive SST anomalies. The surface frontal structure was enhanced under the influence of the upper-level dynamic processes. During the second phase when the medicane formed, low-level diabatic processes determined the evolution of the surface cyclone, without any significant support from baroclinic processes in the upper troposphere. The distortion of the low-level baroclinicity and the frontal structure began after the initial weakening of the upper-level dynamics. During the third phase, the system remained barotropic, being affected by similar mechanisms as in the second phase but with lower intensity. The transition mechanism is not only the result of the seclusion of warm air in the cyclone core but, mainly, the continuation of an explosive cyclone or an intense cyclone when the occlusion began to form.

Precipitation forecasts provided by high-resolution NWP models have a degree of realism that is very appealing to most users of meteorological data. However, it is a challenge to demonstrate whether or not such forecasts contain more skillful information than their lower resolution counterparts. A verification procedure must be based on equally detailed observations that are also realistic in areas where ground observations are not available and remote sensing data can only increase the accuracy of the location of rain events at the cost of decreased accuracy in estimating the amount of rain that has actually reached the ground. Traditional verification methods based on station or grid point comparison yield poor results for high-resolution fields due to the double penalty error that is attributed to finite space and time displacement that such methods do not account for. A complete approach to evaluating precipitation forecasts over complex terrain is suggested. The method is based on realistic gridded precipitation observations generated by an interpolation method that uses long climate data series to determine the geographical characteristics that this parameter is best correlated with as well as remote sensing estimates as background information to cover the areas where observations are insufficient. Spatial verification methodologies are subsequently applied to a convective event that accentuate the relative skill of high-resolution COSMOGR forecasts in revealing characteristics in the precipitation patterns such as structure and intensity.

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

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.

Forecasting in-flight icing conditions is crucial for aviation safety, particularly in regions with variable and complex meteorological configurations, such as Greece. Icing accretion onto the aircraft’s surfaces is influenced by the presence of supercooled water in subfreezing environments. This paper outlines a methodology of forecasting icing conditions, with the development of the Icing Potential Algorithm that takes into consideration the meteorological scenarios related to icing accretion, using state-of-the-art Numerical Weather Prediction model results, and forming a fuzzy logic tree based on different membership functions, applied for the first time over Greece. The synoptic situation of an organized low-pressure system passage, with occlusion, cold and warm fronts, over Greece that creates dynamically significant conditions for icing formation was investigated. The sensitivity of the algorithm was revealed upon the precipitation, cloud type and vertical velocity effects. It was shown that the greatest icing intensity is associated with single-layer ice and multi-layer clouds that are comprised of both ice and supercooled water, while convectivity and storm presence lead to also enhancing the icing formation. A qualitative evaluation of the results with satellite, radar and METAR observations was performed, indicating the general agreement of the method mainly with the ground-based observations.

Data assimilation (DA) integrates observational data with numerical weather predictions to enhance weather forecast accuracy. This study evaluates three regional background error (BE) covariance statistics for numerical weather prediction (NWP) via a variational data assimilation (VAR) scheme. The best practices in DA are highlighted, as well as the impact of BE covariance calculation in DA procedures by employing the Weather Research and Forecasting (WRF) model. Forecasts initialized at different intervals were used to compute distinct regional background error statistics utilizing three control variable (CV) methodologies over a span of 20 days. These statistics are used by the three-dimensional VAR DA process of WRF DA software, producing analysis fields that lead to forecasts for a distinct convective supercell event during the summer of 2019 over northern Greece. This high-impact convective event underscores the importance of selecting appropriate BE over complex terrain areas. The results emphasize the significance of BE usage in DA, proposing the optimal DA approach for simulations of convective systems.