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The weather reanalysis datasets are very advantageous data types worldwide that fill the gaps of missed measuring data and are alternatives that compensate for the scarcity of observed climate data. The main purpose of this study was to evaluate the effect of horizontal distance, altitude, and climatic regions compared to sea level on NASA POWER reanalysis data for daily temperature variables, relative humidity, and wind speed observed in meteorology stations in the Mediterranean and Continental regions of Turkey. For this purpose, three different meteorology stations (Antalya airport, Elmalı, Teffenni) from the Mediterranean region with different distances and elevations compared to sea level and one station (Ankara) far from the Mediterranean region with continental climate were selected. The statistical approach used to compare observed and estimated values in this study was determination coefficient (R2), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Mean Bias Error (MBE). The results showed a high relation between the POWER reanalysis dataset and observed data for all parameters except wind speed. For daily maximum, minimum and mean temperature, the R2 and NSE achieved higher than 0.91 and 0.88 respectively, while the mean bias error MBE ranged between -3 °C up to +2 °C and the RMSE was less than 4 °C in all stations. Additionally, POWER estimated data correlation accuracy for temperature variables increased toward higher altitudes in the study area. Similarly, this performance was followed by relative humidity, increasing relation accuracy toward higher elevated regions. The R2 was higher than 0.69 in higher altitudes and less than 0.4 in lower elevations. The MBE for relative humidity ranges -2% in Antalya to +9% in Ankara, and the RMSE attained less than 13.81% in all regions. The POWER daily wind speed did not show relation with observed data without adjusting for elevation and seasonal bias correction. Overall, it was concluded that the NASA POWER dataset could predict temperature and relative humidity over study area and give a promising result if used in research, water, and agricultural decision-making where observation data are not available. [ABSTRACT FROM AUTHOR]

Currently, the improvement of numerical models of weather forecasting allows using them for hydrological problems, including calculations of snow water equivalent (SWE) or snow storage. In this paper, we discuss the applicability of daily precipitation forecasts for three global atmospheric models: GFS (USA), GEM (Canada) and PL-AV (Russia) for calculating snow storage (SWE) in the Kama river basin for the cold season of 2017–2018. As the main components of the balance of snow storages the following parameters were taken into account: precipitation (with regard for the phase); snow melting during thaws; evaporation from the surface of the snow cover; interception of solid precipitation by forest vegetation. The calculation of snow accumulation and melting was based on empirical methods and performed with the GIS technologies. The degree-day factor was used to calculate snowmelt intensity, and snow sublimation was estimated by P.P. Kuz’min formula. The accuracy of numerical precipitation forecasts was estimated by comparing the results with the data of 101 weather stations. Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). During the snowmelt period, the part of the snow-covered area of the basin was also calculated using satellite images of Terra/Aqua MODIS on the basis of the NDFSI index. The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. It is difficult to determine which model provides the maximum accuracy of the snow storage calculation since each one has individual limitations. According to the PL-AV model, the mean square error of snow storage calculation was minimal, but there was a significant underestimation of snow accumulation in the mountainous part of the basin. According to the GEM model, snow storages were overestimated by 10–25%. When calculating with use of the GFS model data, a lot of local maximums and minimums are detected in the field of snow storages, which are not confirmed by the data of weather stations. The main sources of uncertainty in the calculation are possible systematic errors in the numerical forecasts of precipitation, as well as the empirical coefficients used in the calculation of the intensity of snowmelt and evaporation from the snow cover surface.

Formation conditions are considered and short-term forecast accuracy is estimated for six convective hazardous weather events or their clusters (outbreaks) recorded in the Ural region and eastern part of the European Russia in the warm period of 2020. [ABSTRACT FROM AUTHOR]

The problem of verification of short-term numerical forecast of hazardous and adverse weather using the WRF mesoscale atmosphere model for the territory of the Middle Urals has been considered. The meteorological situations associated with the intense precipitation and convection development in Perm krai and Sverdlovsk oblast (Russia) in 2016 have been studied. The success of heavy snow forecast has been assessed by comparison with the data from weather stations. To forecast heavy snow, data from the GFS and GEM global models have been used along with the WRF model. The comparison with data from the WRF model has demonstrated that the GEM model provides the most accurate forecast. The forecasts of convective phenomena have been assessed with the help of the object-oriented approach based on the comparison of the real and forecasted positions of mesoscale convection-allowing systems. In a number of cases, the WRF model is able to forecast the place and time of adverse weather (including the local ones), which is impossible when the synoptic method is used. The main restriction for numerical forecast of convective hazardous weather is the inability to determine the spatial position of mesoscale convection-allowing systems. Understating the gust velocity of squall winds also occurs, but this problem can be solved by reducing the grid step to 2–3 km.

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach. [ABSTRACT FROM AUTHOR]