Vol. 126, No. 2 * Pages 159–295 * April - June 2022

The joint examination of the climate time series may be efficient methodology for the characterization of extreme weather and climate events. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them.
The first problem can be solved by the standardization procedures, i.e., to transform the variables into standard normal ones. For example, there are the Standardized Precipitation Index (SPI) series for the precipitation sums assuming gamma distribution, or the standardization of temperature series assuming normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the transformed variables defined by their covariance matrix.
We will present the developed mathematical methodology and some examples for its meteorological applications.

The civil war, harsh climate, tough topography, and lack of accurate meteorological stations have limited the number of consecutive synoptic data across Afghanistan. The global data (gridded precipitation datasets) pave the way to assess the precipitation indicators of climate, where stations are sparsely located. This study assessed the mean annual precipitation trend in 33 stations over Afghanistan. Non-parametric linear regression technique was employed to find upward and downward trends and magnitudes. The daily of precipitation was obtained from the database of the CPC-NOAA (Climate Prediction Center - National Oceanic Atmospheric Administration) for the period of 1979–2019. The CPC spatial resolution of daily precipitation is 0.5×0.5 degree. Analysis of mean annual precipitation showed a significant decreasing trend at six provinces in the north, while an increasing trend of 9.2 mm per decade has been observed at three provinces. In the south, a notable reduction of the precipitation trend has been experienced in Helmand, Kandahar, and Nimruz provinces, but Ghazni and Uruzgan show a positive trend. Data revealed that mean annual precipitation has remarkably decreased in the western part of Afghanistan. According to the study period, the mean annual rainfall in the central regions indicates a raise of 37.5 mm per decade in Kabul, while in Vardak, the precipitation increases up to 9.21 mm per year. Eastern regions include 8 provinces, and the eastern highland covers the smallest area that is mainly covered by rangeland and the largest existing forests. These regions are directly influenced by the moist air masses of Indian monsoon getting trapped at the high mountain slopes, and it can lead to an increase of rain. Data reveals an upward trend of precipitation in the eastern part of Afghanistan.

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Târgu Mureș (Marosvásárhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 – May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that – contrary to previous studies carried out on cities in China and India – in most of the urban areas of Marosvásárhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000–2019. Remote sensing data from the MODIS and the Landsat satellites show, that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 °C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 °C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 °C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1–2 °C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons, and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvásárhely had many effects on LST in particular areas that have links to the local economy, trade, and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

In connection with road vehicles and their internal combustion engines, their effects on our environment are being dealt with more and more. Plenty of parameters could be listed, but human catabolism and combustion of liquid fuels probably have not been examined together. Carbon dioxide has the most priority as a greenhouse gas in environmental change and metrology, thus it is a constant topic. Oxygen consumption has been examined rarely or never in such a context. In this article, calculations have been carried out from different points of view regarding these two parameters. The results of total-quantity calculations show, that the oxygen demand for the combustion of fuels used for road transport in 2019 in Hungary is the same as the 6-year oxygen demand of the Hungarian population, and the amount of the carbon dioxide emitted by the combustion of fuel used in road transport in Hungary is the same as the amount emitted by the Hungarian population during 5.2 years. The results might be worth examining on a larger scale.

Assessing the effects of climate change is a key component of the sustainable management of water resources and food security. In this paper, general circulation models (GCM) were evaluated using historical information for Birjand synoptic station, Iran. Modeling was performed using 35 models of the Fifth Climate Change Report for 27 historical periods. The results showed that longer annual periods are the most suitable periods for hydrological simulation when data are available. Therefore, the periods of 1960-1990 may be the most appropriate periods due to the adaptation to the observation data. To estimate rainfall, periods with more years showed a more accurate forecast of the future. Moreover, the results showed more changes in the RCP 8.5 scenario than in the RCP 4.5 scenario. According to the comparison of models, the NorESM1-M model with a root mean square error (RMSE) of 0.091 and the GISS-E2-R model with a low percent bias (PBIAS) can be an appropriate model for estimating rainfall.

Atmospheric precipitation during cyclonic situations was analyzed using weather types classification. Based on data from the period 1991 to 2015, the observed cyclonic situations were analyzed in terms of their frequency of days with a given weather type. Cyclonic situations with airflow direction from the west and northwest, north and northeast, east and southeast, and south and southwest were analyzed. We identified a declining number of days that can be classified as cyclonic situations. The distributions of the mean annual precipitation totals for these cyclonic situations have been investigated. The highest mean annual precipitation totals occurred during the west cyclonic, northeast cyclonic, and east cyclonic weather types. The lowest mean annual precipitation totals were identified during the southwest cyclonic (with fronts moving from north to northeast) and north cyclonic weather types. The percentage of the individual cyclonic weather types and supertypes in the mean annual precipitation total was calculated. The directional supertype west + northwest with the west cyclonic type occurred with the highest percentage, although variations may arise due to windward and leeward effects.

In the last decades of the 1900s, the tipping bucket rainfall gauges (TBG) were used to record the sub-daily rainfall data. In the first period of the rainfall data recording, as a result of the lack of efficient data storage and data transmission, the sampling period of the TBG devices was chosen in a magnitude of 10–20 minutes. Consequently, there are historical datasets characterized by several minutes long sampling periods. Since the turn of the 2000s, the data handling has been revolutionized; the sampling period has diminished to one minute. There is a systematic error of the TBG technique which has been investigated since the middle of the 1900s. Between 2004 and 2008, a comprehensive research was performed to determine the correction equation for several TBG devices. These results can be utilized for the short sampling period measurements (one minute sampling), but for longer sampling period data, further corrections are needed. In this paper, a supplementary correction is presented. On the base of the mathematical determination of the correction factor, simple estimation will be proposed to be able to execute the necessary correction. After the presentation of the correction factor, a general correction factor is proposed for larger geographical regions and wide time span of the measurements. The revision of the historical rainfall data recorded by TBG devices can be important in several issues, such as the re-evaluation of intensity-duration-frequency (IDF) curves, and in other fields.