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Meteorological drought (MDr) causes considerable economic losses in many countries, including in relation to agriculture. To examine the diversity of seasonal meteorological drought in Poland, the study uses monthly precipitation sums in the period from March to November, collected from 74 ground-based meteorological stations in 1951–2020. The paper defines meteorological drought on the basis of the standardized precipitation index in three seasons (SPI-3) and differentiates three degrees of drought intensity: extreme, severe and moderate. The study also calculates the size of Poland's area affected by meteorological drought (AAMDr) and determines the relationship between AAMDr and SPI-3. MDr in Poland occurred more frequently in spring and autumn (every 4–5 years) than in summer (every 7 years). In the areas affected by extreme drought, precipitation was below the average, mainly ≤50% of the climatic norm, and air temperature values were below or above the average of the climatic norm, mainly ranging from −1.0 to 1.0 °C. A significant negative correlation between AAMDr and SPI-3 indices was found. The obtained results could be useful for managing climatic risk and developing regional and local agriculture adaptation plans aimed at mitigating the effects of climate change. [ABSTRACT FROM AUTHOR]

This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas. [ABSTRACT FROM AUTHOR]