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The NWCSAF (Support to Nowcasting and Very Short Range Forecasting Satellite Application Facility) software package provides operational products that ensure the optimum use of meteorological satellite data in Nowcasting and Very Short Range Forecasting. The National Observatory of Athens operates NWCSAF since 2016. The rainfall estimates obtained by the Convective Rainfall Rate (CRR) nighttime algorithm and the Convective Rainfall Rate from Cloud Physical Properties (CRR-Ph) algorithm of the 2016 version are verified against rainfall observations provided by the dense network of automated surface weather stations operated by the National Observatory of Athens (NOA) for a full year. For the verification a temporal upscaling to 30 min was applied to all datasets. Overall, CRR overestimates the extent of the precipitation areas while at the same time it underestimates the precipitation totals. CRR-Ph clearly outperforms the CRR nighttime algorithm regarding the accurate delineation of precipitation areas but it overestimates the precipitation totals. Heavier precipitation is consistently detected by both algorithms although the false alarms rate is high. Seasonal variations are found, with the most important the poorer estimation performance during spring.

Aim of the study is to examine the potential utilization of satellite precipitation data to estimate the peak discharges of flash floods in ungauged Mediterranean watersheds. Cumulative precipitation heights from local rain gauge and the GPM-IMERG were correlated in a scatter plot. The calculated linear equations were used to adjust the uncalibrated GPM-IMERG precipitation data in Thasos island (Northern Greece), to investigate the mechanisms of the flash floods recorded in November 2019 and to evaluate the significance of satellite precipitation data in hydrological modeling. The uncalibrated GPM-IMERG precipitation failed to explain the flash floods phenomena. The rain gauge data are reliable to accurately predict the peak discharges only in cases, where the rain gauges are within the study area. The strong correlation between ground rainfall data and satellite spatiotemporal precipitation data (R2 > 0.65), provides linear regression equations that, through their extrapolation and appliance to the rest of the flooded area, could adjust and correct the satellite data, optimizing the efficiency and accuracy of flash flood analysis, especially in ungauged watersheds. The proposed methodology could highly contribute to the optimization of flood mitigation measures establishment, flood risk assessment, hydrological and hydraulic simulation of flash flood events in ungauged watersheds.

The Price-Rind 92 parameterization (Price and Rind, 1992) that utilizes cloud top height as predictor for the estimation of lightning density is widely used by modelers in an attempt to forecast electrical activity in thunderstorms. In the present paper new parameterizations for the estimation of lightning density of convective clouds are formulated. LINET lightning data, NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short-Range Forecasting) products and ERA-Interim (ECMWF-Re Analysis) data, covering the summer of 2016 over continental Europe, are used. The proposed models estimate the lightning density of convective clouds, using cloud top height, cloud top pressure and cold cloud depth as predictors. Model efficiency statistics calculated over an independent dataset, suggest that the proposed models can be considered successful over the specific area (continental Europe) and period (summer). The new cloud top height model differs from the PR92 and other parameterizations, which is not an unexpected result, since every model has its own characteristics, strengths and weaknesses. The new parameterizations could be utilized in numerical model simulations to produce quantitative estimations of the amount of strokes over convective areas.

International audience; This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.

This paper is devoted to the discussion of the practice of storm naming that has been initiated in January 2017 for the first time in the Eastern Mediterranean Region. Namely the METEO Unit at NOA, taking into consideration that storm naming facilitates meteorologists, researchers, authorities, civil protection officers, the media and citizens to communicate the forecasts of high-impact weather events, started storm naming in January 2017 and has named 35 storms up to September 2021. The criteria of storm naming are discussed, and a synopsis of the events is presented. The monthly distribution shows that 57% of the named storms occurred during the winter period, with January being the month with the highest percentage of occurrence of named storms (28%). The impact of storm naming on citizens risk perception and increased awareness has been also assessed through an internet-based questionnaire that was launched on the fourth year of the storm naming practice in Greece. Overall, results indicate a significant impact of storm naming on the readiness of citizens through the activation of perceptual and cognitive mechanisms.

An analysis of selected spatiotemporal characteristics of isolated thunderstorms in relation to cloud-to-ground CG lightning over part of the eastern Mediterranean is performed. The purpose of the study is twofold: to better understand and improve the basic knowledge of the physical mechanisms of the phenomenon and to offer new means of nowcasting the lightning activity in such thunderstorms. Meteosat Second Generation MSG Rapid Scan Service RSS infrared imagery which offers the option of tracking the examined storms with a time frequency of 5 minutes is one of the two utilized datasets, the other being ZEUS very low frequency VLF lightning detection system’s CG discharge data. It was shown that a cloud top temperature of about −20°C is required for the onset of lightning activity. A rapid drop of the cloud-top temperature of about 11°C in 5 minutes on average is observed a few minutes before or during lightning initiation. The maximization of the activity is usually quite close to the overall minimum cloud-top temperature of the cell. A temperature increase of 3.5°C from this overall minimum can mark the end of the activity, which is also associated to the time evolution of the cell’s horizontal extent. In fact, after the cell’s horizontal area stops to increase and/or starts to gradually diminish, CG lightning activity is expected to stop.

The development and efficiency assessment of a lightning activity nowcasting tool is presented. The tool employs MSG IR imagery and real-time lightning data provided by ZEUS network to nowcast the manifestation of lightning activity over the Greek mainland for a time span of 1 h. The efficiency of the tool is assessed for 20 days with widespread lightning activity observed during the warm period of the year through a verification procedure that computes a collection of appropriate statistics for selected areas. The analysis of these statistics shows that the tool estimates successfully almost 80% of the upcoming activity. The false alarm rate is close to 40%, while a small overestimation is evident. Since the adverse effects of a case of missed activity are much more than that of a false alarm, the tool is considered successful and fit for operational use.

Shipping is a growing transport sector representing a relevant share of atmospheric pollutant emissions at global scale. In the Mediterranean Sea, shipping affects air quality of coastal urban areas with potential hazardous effects on both human health and climate. The high number of different approaches for investigating this aspect limits the comparability of results. Furthermore, limited information regarding the inter-annual trends of shipping impacts is available. In this work, an approach integrating emission inventory, numerical modelling (WRF-CAMx modelling system), and experimental measurements at high and low temporal resolution is used to investigate air quality shipping impact in the Adriatic/Ionian area focusing on four port-cities: Brindisi and Venice (Italy), Patras (Greece), and Rijeka (Croatia). Results showed shipping emissions of particulate matter (PM) and NOx comparable to road traffic emissions at all port-cities, with larger contributions to local SO2 emissions. Contributions to PM2.5 ranged between 0.5% (Rijeka) and 7.4% (Brindisi), those to PM10 were between 0.3% (Rijeka) and 5.8% (Brindisi). Contributions to particle number concentration (PNC) showed an impact 2-4 times larger with respect to that on mass concentrations. Shipping impact on gaseous pollutants are larger than those to PM. The contribution to total polycyclic aromatic hydrocarbon (PAHs) concentrations was 82% in Venice and 56% in Brindisi, with a different partition gas-particle because of different meteorological conditions. The inter-annual trends analysis showed the primary contribution to PM concentrations decreasing, due to the implementation of the European legislation on the use of low-sulphur content fuels. This effect was not present on other pollutants like PAHs. (C) 2016 Elsevier Ltd. All rights reserved.

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.

Climatic aspects of extreme European precipitation are studied. Daily pluviometric data from 280 stations across Europe, covering the period from 1958 to 2000, are used. First, the criteria for extreme precipitation cases and episodes are communicated using threshold and spatial definitions. The cases and episodes meeting these criteria are grouped according to their area of appearance. Most of them are located in three major areas: Greece, the Alps, and the Iberian Peninsula. The existence of trends in the annual and seasonal time series of these extreme events is examined. Decreasing trends are found in most of the cases, for Greece, the Iberian Peninsula, and Europe, as a whole. The Alps present a different behavior, with no trend at all in the southern part, and a possible increasing trend in the northern part. Finally, the positive impact of altitude in the frequency of occurrence of extreme precipitation episodes in Europe is discussed.

This paper presents an analysis and discussion of some rainfall characteristics of the European continent. Harmonic and Spectral Analysis are applied to monthly and annual precipitation data derived from 50 selected European meteorological stations. The Harmonic Analysis reveals that the sum of the first and second harmonic describes satisfactorily the mean annual precipitation regime, while the time of maximum of the first harmonic coincides with the time of the observed mean annual maximum of precipitation, almost everywhere. Spectral Analysis reveals the existence of QBO and ENSO signals in some different areas of Europe, while the eleven-year cycle of sunspots seem to have no effect on precipitation across the European continent. Climatic noise is also examined. The lowest values of climatic noise are found in the British Islands and in France, whilst relatively high values are found in the cyclogenetic region of the Gulf of Genoa. An increase in climatic noise has been identified over Mediterranean coastal areas during the summer.

The paper presents a comprehensive analysis of the most basic features of the air quality of a Mediterranean urban environment area. The impact of meteorology on the air quality is also examined. Observational surface concentrations of the most important air pollutants, recorded at two measuring stations in Patras, Greece, are used. The Weather Research and Forecasting (WRF) meteorological model was employed to produce a series of surface and upper air data and local circulation and ventilation indices. These modeled data along with selected surface meteorological observational data comprise a substantial data set that was used to assess the effect of meteorology on the air quality. Mostly during the summer period, a significant proportion of the particulate matter is transported from sources away from the measuring sites. The synoptic setting of winter and summer seasons represented primarily by the local ventilation and recirculation, the wind, the boundary layer height, and the precipitation has a very strong impact in the overall formation of the air quality status.

The main aim of this work is the investigation of the contribution of different emission sources to the concentrations of the fine Particulate Matter (PM2.5) in the city-port of Patras in Greece using a meteorological and a photochemical model. Emphasis is given on the contribution of the maritime transport and the activities within the Patras harbor area for which the existing environmental information is very limited.

This study aims at investigating the relationship of climatic variables with geographic features over Greece. Seasonal and annual climate normals of temperature, rainfall and relative humidity covering the period from 1975 to 2004, from 85 meteorological stations of the Hellenic National Meteorological Service network are included in the analysis. Several geographical and environmental parameters such as altitude, location, slope, aspect, distance to coast, sea/land ratio and vegetation index obtained from a coastline dataset, a digital elevation model and a land cover database were examined for their dependencies with climatic elements. The relationship of a climate element with each geographical variable was investigated by means of graphical (e.g. scatter plots) and statistical scores. The results were used to assess the ability of each geographical parameter to explain part of the spatial variability of a climate variable. Backward stepwise linear regression was used to obtain a surface that gives the best fit to the measured climatic data. The adjusted determination coefficient (R 2 adj ) was computed to evaluate the efficiency of each model. Elevation, latitude and NDVI were found to be the most important predictors, whereas other geographical parameters contribute less to the model predictability.