This study presents an analysis of 40-years (1981-2020) precipitation climatology of ERA5-Land and E-OBS gridded datasets over Greece. The analysis focused on trends in total annual, low, moderate, and extreme precipitation-as well as number of rainy days-corresponding to the climatically diverse geographical areas of Greece. Substantial differences were found in the results between the two datasets that could be attributed to the nature of data sources. In general, ERA5-Land revealed slightly negative trends in total precipitation amount, an increase in low and moderate precipitation intensity, and an overall positive trend in extreme precipitation intensity. E-OBS demonstrated a rather negative trend in total precipitation amount, a clear decline in Crete in low precipitation intensity, a positive trend in northern Greece, and a negative trend in Southern Greece and Crete for moderate intensity, while no clear trend was revealed for most parts of Greece for extreme precipitation intensity. Regarding the corresponding results for the number of rainy days (NRD), a significant reduction was evident for E-OBS data and a slight increase for ERA5-Land data for total precipitation amount. Analysis of low precipitation intensity indicated a slight increase for ERA5-Land and an overall reduction for E-OBS. The differences are less pronounced for moderate precipitation intensity, while for extreme precipitation intensity, differences were considered as rather localized. Finally, both datasets showed positive trends in northern or mountainous geographical areas while exhibiting controversial results mainly over southern and coastal zones.

Assimilation of weather radar data, as well as additional data like lightning data, in high-resolution weather forecast models is a promising method to improve short-term forecasting (nowcasting) of flash-flood events. A data assimilation system based on the Weather Research and Forecasting model is used in this study, with radar reflectivity and radial velocity data collected with X-band Doppler polarimetric radar in the area of Athens, Greece, and lightning observations obtained from a lightning detection network covering Greece. Radar data are assimilated with the four-dimensional variational method, which includes a full-hydrometeor assimilation scheme, in a nested domain of the model with a resolution of 3 km. Humidity, vertical velocity and horizontal wind divergence profiles estimated from lightning data are assimilated with a three-dimensional variation method in the parent domain of the model with a resolution of 9 km. The results from a case study are presented to show the effect of assimilating each type of data.

The aim of this study is to examine the impact of poor air quality and adverse meteorological conditions on health risks in the Greater Athens Area (GAA), Greece, during the period from 2018 to 2022. Specifically, the aim is to assess the Relative Risk (RR) of hospital admissions (HAs) for cardiovascular diseases (CVDs) and respiratory diseases (RDs), due to air pollution in combination with thermal discomfort, as well as to identify the time lag effect on admissions. For this purpose, data from six (6) different hospitals within the GAA were collected and used. Statistical analysis of hourly measurements of key pollutants (NO2, O3, PM2.5, and PM10) obtained from the Directorate of Climate Change and Air Quality (DCCAQ), which falls under the auspices of the Ministry of Environment and Energy (MEE), and meteorological parameters (T, RH, and wind velocity), is performed to calculate the daily air quality and human thermal comfort–discomfort levels, respectively. These conditions were examined using appropriate indexes for both air quality and human thermal comfort–discomfort, as independent variables in a Negative Binomial regression model developed in R, with daily HAs (not including scheduled cases or pre-existing health conditions) as the response variable. Moreover, a spatiotemporal analysis of air quality and meteorological parameters is conducted to identify associated variations in health risks. This analysis highlights key risk patterns linked to environmental conditions and the relevant measures to both manage and mitigate the risk. Findings indicate that extreme environmental conditions significantly elevate health risks, with cumulative RR over a one-week period peaking at 1.540 (95% CI: 1.158–2.050) during the warm season, while prolonged increases in the RR are also observed during the cold season, reaching 1.214 (95% CI: 0.937–1.572) under extreme cold exposures.

We report on the development and validation of a neural network (NN) model of PM10 concentrations in terms of photochemical measurements of NO, NO2 and O3 and temporal parameters that include the day of the week and the day of the year with its sinusoidal variation. A long-term record (≈10 yr) from 2001–2012 (inclusive) assembled from measurements taken at 10 station nodes in the air quality monitoring network of the Greater Athens Area in Greece has been used. Eight synoptic categorizations of the circulation at 850 hPa were used to partition the data record, and to train individual NNs with Bayesian regularization using 90

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.

In recent decades, large-scale urbanisation has developed rapidly, resulting in significant changes in the local and regional environment and climate. Large metropolitan areas worldwide induce significant changes in local atmospheric circulation and boundary layer meteorology by modifying the underlying surface characteristics and through the emission of anthropogenic heat and pollutants into the atmosphere. We investigate the urban heat island (UHI) characteristics in the city of Athens, Greece, which is one of Europe’s largest metropolitan complexes with a population of approximately 3.7 million inhabitants. The UHI effect is intense due to the city’s size, dense construction, high incident solar radiation, and almost complete lack of natural vegetation, with previous studies suggesting a temperature rise of 4 °C on average in the city centre compared to summer background conditions. We used high-resolution WRF simulations (1-km horizontal grid) driven with ERA5 reanalysis data to produce surface temperature maps in the city of Athens and the surrounding areas (Region of Attiki) during the summer period of 1 July–20 August 2021. Different model parameterizations were tested, both with respect to urban characteristics and physical parameters. The daily minimum and maximum temperatures (Tmin and Tmax) derived from the model were validated against observational data from a dense network of weather stations covering metropolitan Athens and surrounding locations. We further investigate the influence of different meteorological conditions on the UHI gradients as produced by the model and the observational dataset, including the extreme heat wave of 28 July–5 August 2021, during which persistent maximum temperatures of >40 °C were recorded for nine consecutive days. The results indicate a strong correlation between WRF output and recorded minimum and maximum temperatures throughout the test period (R ranges from 0.80 to 0.93), with an average mean absolute bias (MAB) of 1.5 °C, and reveal the intensity and spatiotemporal variability of the UHI phenomenon in the city of Athens, with UHI magnitude reaching 8–9 °C at times. Our work aims to maximise the potential of using high-resolution WRF modelling for simulating extreme heat events and mapping the UHI effect in large metropolitan complexes.

Global Precipitation Measurement (GPM)-Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) precipitation estimates are evaluated against the gridded dataset "ENSEMBLES OBServation" (E-OBS) over the Mediterranean region for the period 2000–19. It was found that both satellite precipitation products presented a good agreement in terms of correlation values on annual basis, although presenting lower values at mountainous and coastal areas and areas that E-OBS dataset has limitations of gauge rain station records. However, in general, IMERG presented better ability to adjust precipitation in comparison to GSMaP.

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.

Moderate Resolution Imaging Spectroradiometer MODIS, land surface temperature data, during daytime LSTday or night-time LSTnight, were employed for predicting maximum Tmax or minimum Tmin air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration ET models in 28 stations in Greece during the growing season May–October. The deviations between daily LSTnight and Tmin were found to be small, but they were greater between LSTday and Tmax. Furthermore, the temperature vegetation index TVX method was employed for achieving more accurate Tmax values from LSTday, after determining the normalized difference vegetation index of a full canopy NDVImax. The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the Tmax estimates from LSTday was not encouraging, for being used operationally over Greece. Thus, LSTday or LSTnight MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains REMCT] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions r = 0.93, mean bias error = 0.44 mm day–1 and root mean square error = 0.74 mm day–1, which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

Climate change affects the precipitation regime globally, including its extreme values, as well as the frequency of the corresponding extreme events. Generally, observations and simulations with climate models show that a warming climate typically results in an intensification of precipitation extremes. An analysis of the daily precipitation database for the island of Cyprus is performed, using a period of 50years. A number of climatic indices for precipitation are calculated, including the Standardized Precipitation Index (SPI), using the dense network of rain gauges of the Cyprus Department of Meteorology. All parameters are calculated for the recent 30-year period 1981–2010 (considered by the World Meteorological Οrganization as the new current standard period); the results are compared with those of the period 1961–1990 (considered by the World Meteorological Organization as the reference period), with a view to examine the differences in the occurrence of wet and dry periods. The results show an increase in the occurrence of heavy rainfall events and a slight decrease in extreme drought events.

The influence of climate change on the precipitation extremes is examined using results from theory, modeling and observations. Observations and simulations with climate models show that a warming climate typically results in an intensification of precipitation extremes. Towards this direction, an analysis of the daily precipitation database for the island of Cyprus is performed, for a period of 50 years. A number of climatic indices for precipitation are calculated using the dense network of rain gauges of the Cyprus Department of Meteorology. These parameters are calculated for the recent period spanning from 1981 to 2010. The results are compared with those of a previous period, namely the period 1961–1990, in order to investigate the changes regarding the occurrence of extreme rainfall events, along with the differences in the recording of periods with drought, showing a decrease in the number of rainy days along with the number of days with heavy rainfall.

The Mediterranean region is a unique area characterized by a large spectrum of atmospheric phenomena, some of which have a high impact on many aspects of human activities, safety and wellbeing. The area is long considered as a hot spot of such atmospheric phenomena deserving multidisciplinary scientific attention. The scientific research that has been carried out on these high impact atmospheric processes that occur in the Mediterranean area is indeed widespread and the available international literature is very extensive. The paper touches initially the temperature and precipitation regimes, followed by a discussion of floods and droughts. The exciting cyclogenetic patterns of explosive cyclones and medicanes are presented in separate sections. The lightning activity and the presence of dust and other pollutants are also presented herein. The atmospheric chemistry of the region which is increasingly becoming of utmost importance for the area under study is distinctly discussed. Attempts to modify the weather (the precipitation, in particular) are outlined too. The effects of climatic change on various atmospheric processes are considered throughout this paper, in addition to a dedicated section on temperature and precipitation.

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration-Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the highresolution of the available data, since the inherent 0.05 degrees resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel down-scaling method in order to attain high resolution (1 km x 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km x 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.

This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM 10 (particulate matter with aerodynamic diameter less than 10 μ m) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM 10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.

The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5kmx5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1kmx1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1kmx1km and precipitation measurements from the official raingauge network of the Cyprus' Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks' architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models' performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.