Vol. 125, No. 2 * Pages 167–336 * April - June 2021

Cold air pool (CAP) is a winter-time, anticyclonic weather event: a cold air layer confined by the topography and warm air aloft. If its duration is more than one day, then it is called persistent cold air pool (PCAP). CAPs are mainly examined in small basins and valleys. Fewer studies pay attention to PCAPs in much larger basins (with an area of more than 50 000 km²), and it is not evident how effective the existing numerical definitions are in cases of extensive PCAP events. A possible method of identifying PCAPs in a large basin is to identify PCAP weather conditions at different measuring sites across the basin. If there are PCAP weather conditions at most of the sites, then it is likely to be an extensive PCAP.
In this work, we examine which of the documented CAP definitions can be used for reliable local detection of CAP conditions. Daily weather reports and meteorological data from two locations in the 52 000 km² sized Great Hungarian Plain have been used to obtain a reference set of days with PCAP weather conditions during two consecutive winter months. Several numerical CAP definitions were compared for their performance in recognizing the presence of PCAP weather conditions using radiosonde measurements and reanalysis data. The lowest error was produced by using the heat deficit (HD) method. So this is considered the most suitable method for local identification of PCAPs in the Great Hungarian Plain.

In this paper, evaluation the performances of GEP (gene expression programming), ANFIS ( adaptive fuzzy interference system), and SVM (support vector machine) artificial intelligence models in two scales of daily and monthly rainfall data from Urmia meteorological station (Iran) and monthly rainfall data from Diata meteorological station (India) was used in rainfall simulation. The correlation coefficient of observed and simulated values was evaluated by the R² criterion, simulation error was evaluated by the root mean square error (RMSE), and MB criteria and model efficiency were evaluated by the Nash-Sutcliffe method. The results show that the correlation coefficients in the GEP model based on daily data from Urmia station and monthly data from Diata station are 23 and 58%, respectively, and R² in simulation with GEP is estimated to be 55% lower than with the other two models. The R² range in both ANFIS and SVM models varies from 91 to 93%. On average, the RMSE values in the GEP simulation are 50% and 55% higher than the ANFIS ratio for daily and monthly data at the two stations, respectively, and the RMSE values of ANFIS model are 1% and 3% higher than those of the SVM at Urmia and Diata stations, respectively. The bias values of the GEP model are 72% and 60% higher than those of ANFIS at Urmia and Diata stations, respectively. The GEP efficiency factors are 56% and 61% lower than those of ANFIS at Urmia and Diata stations, respectively. And the ANFIS efficiency ratio is 1 and 2% lower than SVM in Urmia and Diata stations, respectively. Therefore, rainfall simulation with the SVM model is associated with a lower error rate and better efficiency, the ANFIS model is close to the efficiency of SVM, and the GEP model is not suitable for rainfall simulation.

In this paper, the mathematical models of a solar thermal system which governs the solar thermal collector, the heat storage system, and the pump are presented. It has been shown that it is possible to connect a Simulink-based model to a meteorological database server using standard communication protocols by a C language-based component in order to import real-life weather information into the simulation. The setup of the model predictive control of this solar thermal system and the results of the simulation are also presented. This computationally heavy control method is possible to use on today's personal computers, and it can be expanded.

The paper presents trends for three categories of variables: average annual, average maximum and average minimum air temperatures. Data was provided by the meteorological yearbooks of the Republic Hydrometeorological Service of Serbia. The main goal of this paper is to detect possible temperature trends in Central Serbia. The trend equation, trend magnitude, and Mann-Kendall non-parametric test were used in the analysis of climate parameters. The used statistical methods were supplemented by GIS numerical analysis, which aimed to analyze the spatial distribution of isotherms from 1949 to 2018. The obtained results indicate that out of the 72 analyzed time series, an increase in air temperature is dominant in 61 time series, while 11 time series show no changes. The highest increase was recorded in the average maximum time series (4.2 °C), followed by an increase of 3.5°C in average maximum air temperatures. The highest increase in the average annual time-series was 3.0 °C. The lowest increases in air temperature were recorded in the average minimum time series (0.1 and 0.2 °C). In two average minimum time series a decrease in average air temperatures was identified (-0.6 and -0.4 °C. The application of GIS tools indicates the existence of interregional differences in the arrangement of isotherms, leaded by the orography of the terrain. In the spatial distribution of the analyzed variables, "poles of heat" and "poles of cold" stand out, and the influence of the urban heat island is evident (especially in the case of the urban agglomeration of Belgrade). The manifested spatial patterns of air temperature need to be further examined and the correlation with possible causes need to be determined. For these reasons, the paper provides a solid basis for studying the climate of this area in the future, as it provides insight into climate dynamics over the past decades.

The regional response over Southeast (SE) Europe to the climate warming in global and continental scales has been confirmed to have essential impact on the agriculture and forestry since the middle of twentieth century. Normal variations in weather throughout a growing season cause variations in harvest and, generally, the impact could be large in terms of production amounts and economic returns. Agriculture is sensitive to the changes in weather and climate, and the occurrence of extreme events threaten the agricultural systems. Forests are particularly sensitive to climate change, because the long life-span of trees does not allow for rapid adaptation to environmental changes. This study provides an overview of the spatial patterns and the long-term temporal evolution of the following agrometeorological indices: growing season length, accumulated active temperatures and biologically effective degree days. Hence the focus is on the Growing season length, its start and end dates are analyzed separately. All indices are computed from the daily mean temperatures which, in turn, are derived from the output of the MESCAN-SURFEX system analysis of the collaborative initiative UERRA. The geographical domain of interest is Southeast Europe, and the assessment is performed at a very high spatial resolution on annual basis for the period 1961–2018. We find strong evidences of essential increase in the considered indices which dominates spatially over the low-elevated areas of the domain and is statistically significant at 5% level. Key message is also the revealed asymmetry of the increase in the most relevant index, the growing season length: its total lengthening is linked more to the shifting to earlier date of the start, rather than to its later cessation.

The study presents analysis of current air pollution state over Ukraine including remote regions and uncontrolled Ukrainian territories; features of NO₂, SO₂, and CO spatial distribution and seasonality under the influence of local anthropogenic emissions. The research is based on Sentinel-5P satellite data for the period of November 2018 – January 2020. Despite the increasing traffic emissions, the industrial emissions still greatly influence the air pollution in Ukraine. Sentinel-5P coverage allowed detecting a number of cities with huge anthropogenic NO₂ and SO₂ emissions, where ground-based measurements are absent. Uncontrolled territories on the east part of Ukraine still negatively affect air quality in the region due to the activity of coal-fired thermal power plants. The study indicates significant air quality changes during the heating season in winter and open burning in March – April. There were found differences in NO₂ seasonal variability over clean remote regions and industrial zones. The paper analyzes features of shipping emissions during the tourist season for Ukrainian coastline of Black and Azov Seas, showing huge negative impact of chaotic movements of tourists boats near the Dzharylhach National Nature Park.

Water scarcity and the climate change impacts on water components will drastically alter everybody's life. The Soil and Water Assessment Tool (SWAT) has been utilized in this study in combination with Sequential Uncertainty Fitting Program (SUFI-2) to simulate precipitation (P), temperature (T), blue water (BW), green water flow (GWF), and green water storage (GWS) in Kashafrood River Basin, Iran. The outputs of two Coupled Model Intercomparison Project Phase 5 (CMIP5) models (MIROC-ESM and GFDL-ESM2G) are selected for hydrological modeling under Representative Concentration Pathways (RCPs) of 4.5 and 8.5 and for the near future (2014-2042) and far future (2043-2100( periods compared to historical period (1995-2011). The results of RCPs, in comparison with the historical period, show that P and BW are increased and in GFDL-ESM2G are better than MIROC-ESM, while T tends to increase, and MIROC-ESM is better than GFDL-ESM2G. GWF, in all future periods (except in MIROC-ESM in near future and under RCP4.5 and 8.5) and in all RCPs tend to decrease, and the results of MIROC-ESM are better than those of GFDL-ESM2G in near future and are vice versa in far future. It is anticipated that GWS continues its historical trend in the future.

Based on daily meteorological data, spatial and temporal distributions of extreme precipitation in 1961–2018 were examined for the North Caucasus and the Crimean Peninsula. Extreme precipitation indices recommended by the Expert Team for Climate Change Detection and Indices were calculated for 45 meteorological stations. Analysis shows that the highest values of extreme precipitation indices are on the Black Sea coast of the Caucasus, except duration of dry spell, because of the atmospheric circulation features and the complex orography of studied area. Extreme precipitation trends are spatially incoherent and mostly statistically insignificant over the studied territory. Significant upward trends on the Caspian Sea coast and Stavropol Upland and statistically significant decreasing trends in the fixed threshold-based indices and all intensity indices over the Crimean Peninsula were detected. Positive and significant correlation between precipitation indices (except consecutive dry days) and altitude was obtained.