Vol. 125, No. 1 * Pages 1–166 * January - March 2021

The present study examines the origin and environmental conditions of the severe convective windstorm on September 17, 2017, which affected several countries in the central and southeastern Europe, above all Serbia and Romania. The large area of the damage swath (at least 500 km long) and high wind gusts (up to 40 m/s) would classify this event as a derecho or at least as a storm very similar to derechos (with respect to newer definition proposals). Small-scale bow echoes were found in areas with highest reported wind gusts, and some thunderstorms within the storm-producing convective system were probably supercells. The existence of high wind shear and storm rotation could be also related to the significant rightward deflection of the system with respect to the mean wind and propagation of other thunderstorms and systems observed on that day. In contrary to many other known derecho events, this storm propagated toward a very dry airmass exhibiting only low or moderate convective available potential energy (CAPE) values. This is shown by soundings, ECMWF model outputs, and vertical profiles from the IASI L2 satellite sounder. Several convective parameters (e.g. CAPE, downdraft CAPE, derecho composite parameter, 0-3-km  relative humidity, 0-6-km shear) were evaluated and compared with proximity soundings of other described European derechos or with the available climatology. The possibility of a balance between the cold pool-generated horizontal vorticity and the environmental shear is also discussed. It is concluded that identification of low-level humidity sources (with aid of storm-relative wind vectors or streamlines) can be important in forecasting of thunderstorm systems moving toward an airmass, which is seemingly too dry for development and maintenance of deep convection. It is also shown that due to low CAPE values, some composite parameters would not indicate favourable conditions for a long-lived convective system. The lack of radiosonde observations can be partially supplemented by data from the IASI L2 sounder, which profiles can be largely different from model forecasts, showing much drier air in the mid- and upper troposphere in this case. It is concluded that due to the absence of strong synoptic forcing and larger pressure gradient at surface, convective processes played major role in the windstorm development. The presence of high temperature lapse rates at low- and mid-levels, high wind shear and unusually dry pre-storm airmass could be considered as the most important signatures related to the storm severity.

The present paper analyzes 549 severe weather events reported to the ESWD (European Severe Weather Database) that caused large hail in the territory of Romania.
Values of atmospheric instability indices have been analyzed for these episodes using data from Bucharest and Budapest sounding stations. For a period of 140 days with episodes of large hail, 24 instability indices were analyzed to describe the atmospheric conditions of the main daily convective activity.
The mean values for most indices characterize an unstable atmospheric environment. Of the indices that measure potential instability, VT (vertical totals index) and TT (totals index) had values that described a conductive atmospheric environment for the development of hailstorms. In addition, the interquartile values of LIV (lifted index using virtual temperature) had values lower than zero. For SWEAT (severe weather threat index) and CAPEV (convective available potential energy index using virtual temperature), only the values in the 75th percentile describe a very unstable environment (according to the literature).
Strong linear correlations were registered between several pairs of indices such as CAPEV-LIV and SWEAT-SI that can be used for the operational forecast of hail.

This study analyzes how outdoor temperature influences domestic hot water consumption in multiapartment large-panel system buildings in Budapest, Hungary. The analysis is based on data from the validated invoicing system of the district heating provider, and from two weather stations of the Hungarian Meteorological Service. The official monthly hot water consumption data of 72 buildings for 7 consecutive years and the corresponding monthly mean temperatures were used in this study. Linear regression analysis and time series decomposition were carried out. The results prove that the outdoor temperature and the domestic hot water consumption are definitely related. The model based on regression analysis could account for 74% of values. The time series decomposition model is able to estimate hot water consumption per apartment per day for a future month with 94% probability. The study relies on data obtained from a projection of two regional climate models each, namely ALADIN-Climate and RegCM. Based on these data, the model forecasts how the effects of climate change will probably influence domestic hot water consumption in the near future. These results shed light on the factors influencing hot water consumption, and may help authorities and decision makers to form sustainability policies and to plan sustainable resource management.

Precipitation variability analysis, on different spatial and temporal scales, has been of great concern during the past century because of the attention given to global climate change by the scientific community. According to some recent studies, the Iranian territory has been experienced a precipitation variability, especially in the last 50 years, and the arid and semi-arid areas seem to be more affected. The present study aims to analyze precipitation extreme indices over a wide time interval and a wide area, detecting potential trends and assessing their significance. The investigation is based on a wide range of daily and multi-day precipitation statistics encompassing basic characteristics and heavy precipitation. Two different methods of trend analysis and statistical testing are applied, depending on the nature of the statistics. Linear regression is used for statistics with a continuous value range, and logistic regression is used for statistics with a discrete value range. The trends are calculated on annual and seasonal bases for the years 1951–2007. Statistical analysis of the database highlight that a clear trend signal is found with a high number of sites with a statistically significant trend. In winter, significant increases are found for all statistics related to precipitation strength and occurrence. In spring, statistically, significant increases are found only for the statistics related to heavy precipitation, whereas precipitation frequency and occurrence statistics show little systematic change. The trend signal is strongest in highlands and mountainous terrains. In autumn and summer, the heavy and basic precipitation statistics did not show statistically significant trends.

The hypothetical climate change and the stress influences caused by the increasingly frequent found meteorological extremities affect the fertility of soils in even more degree. During our soil-climate sensitivity researches, the expression of the drought sensitivity as a stress influence, evolved as a result of lack of precipitation in soil fertility was studied. During our work, effects of increasing droughts of last decades were investigated through the yield results of the three most important crops, winter wheat (Triticum aestivum L.), corn (Zea mays L.), and sunflower (Helianthus annuus L.), based on the area rate in the Hungarian sowing structure, in relation to the natural geographical microregions and fertility of sites. For the examinations, yield data of the National Pedological and Crop Production Database (NPCPD) were used. The database contains complex plot-level crop production and soil information for 5 years (1985–1989). The examination results prove the considerable drought sensitivity of that lands, where soil types with high sand or clay content can be found. The mainly exposed microregions for the effects of drought are, e.g., the Dorozsma-Majsa Sand Ridge, Kerka Riverscape, Dévaványa Plain etc., while less sensitive sites are e.g. the Enying Ridge, Tolnai-Sárköz, Nógrád Basin etc.

Regional reanalises are attractive new sources of meteorological data for the growing society of the end users, due to their physical consistency, dynamical coherency, and multivariate products at higher than the global reanalises spatio-temporal resolution. The assessment and quantification of uncertainties of the products of the regional reanalises and their added value are crucial for the interpretation. Hence these products could be also incorporated in the regional climatology, consistent comparisons of their long-term timeseries against independent and representative data sets have to be performed. The present study could be considered as step ahead in this direction - the MESCAN-SURFEX, which is the product with the most detailed spatial structure among all others in the UERRA (Uncertainties of Ensembles in Regional Reanalysis) project, is compared against two gridded observational data sets in South-east Europe: the well-known regional CARPATCLIM and the product of the Bulgarian National Institute of Meteorology and Hydrology ProData. The comparison aims to assess the skill of MESCAN-SURFEX to reproduce the climatological field of the mean temperature. Additionally, the daily extreme temperatures are estimated using the MESCAN-SURFEX output on sub daily basis and the results are compared against their CARPATCLIM- and ProData-counterparts. The computation of the mean and extreme temperatures with the MESCAN/SURFEX data are performed for the whole time span of this product and the comparison against the references for the whole time span of each of them on daily basis. The main conclusion of the study, which agrees with the outcomes of more detailed recent evaluations, is that MESCAN-SURFEX reproduces realistically the regional temperature field over Southeast Europe. According to the mean temperature, the differences remains under certain limits (RMSE generally below 2 °C) without, at least not apparent, systematic and spatial pattern. The estimation of the extreme temperatures produces results with biases comparable to the biases of the mean temperature, which makes the proposed method applicable for certain cases.

Observation was conducted to determine the impact of water deprivation under flowering on the yield components of soybean at Keszthely, in the growing seasons between 2017 and 2018. The soybean represents an artificial ecosystem in this study. Three water levels designated as full watering in traditionally operated evapotranspirometer (ET), water withdrawal under crop flowering in modified evapotranspirometer (RO), and rainfed (P) crops were used. In RO treatments, the crops received half of the water based on the amount of unlimited water supply. Irrespective of variety, the highest water uses were obtained in ET, while the lowest ones were observed in RO over both growing seasons. Surprisingly, in spite of different variety standards provided by the crop breeders, and irrespective to water supply, no significant impact in actual evapotranspiration rate, ET_a, between the two varieties was observed. Significant impact in soybean water losses between the treatments was observed in RO as compared to the evapotranspiration of crops with unlimited watering.

Dust storm is a natural hazardous phenomenon that affects arid and semi-arid regions of the world the same as Iran. The present research aims to investigate the formation of synoptic patterns of pervasive dust storms (PDSs) in the southwestern regions of Iran. For this purpose, daily data of visibility less than 1000m in 16 synoptic stations (Ilam and Khuzestan provinces) were reviewed during 2004–2017, and 59 PDSs with more than 2 days of duration (overlapped: 70% of the region) were extracted. In practice, mid-level atmospheric data (500, 700, 850 hPa, and sea level pressure (SLP)) with 2.5*2.5 degree resolution (domain: 0-80°E and 10-70°N) were obtained from NCEP/NCAR reanalysis dataset, and the matrix 825*59 of 500 hPa data was performed. Principal component analysis (PCA) with S-mod, were used for extracting synoptic patterns that make PDSs. PCA showed that the first four components ensured more than 86.45% of the data variance. PDSs classification based on output components showed that the patterns had seasonal structures. Synoptically, the north wind blowing in the first pattern is the most dominant structure in the formation of PDSs in the Middle East. The second and third patterns showed postfrontal structures. The fourth pattern with prefrontal structure was the reason for PDSs in the cold seasons of the year. From the four final patterns, the first three patterns showed the dominance of the Persian trough in the SLP maps. Mean values map analysis of the aerosol optical depth suggests that each of the most consistent synoptic patterns stimulates special dust centers.