Vol. 126, No. 4 * Pages 425–582 * October - December 2022

This study presents the results of research that was conducted in the city of Tehran, located in the subtropics (35° N.) in a semi-desert climate in southwest Asia. The purpose was to analyze the relationship between land use/cover change (LULC) and the spatiotemporal dynamics of surface urban heat islands (SUHIs) and give results regarding the structure of the UHI in the city of Tehran. Using Landsat-5 TM data from 1986 to 2010, we quantified the spatiotemporal variability of the SUHI and LULC in the city of Tehran. The spatial distribution of land surface temperature (LST) showed the most extensive SUHI as spatially located in the western and southwestern areas of Tehran in 1986. In 2010, the spatial extent of SUHI had increased. The occurrence of LULC changes in the southern, southwestern, and especially the western parts of Tehran have played the most important role in expanding and intensifying the SUHI effect. These areas experienced two major alterations: (a) The area lost about 14 km² from green cover; and (b) the industrial and commercial land use, and transportation network extended significantly in these areas. Based on LULC and LST distribution patterns, barren lands, industrial and commercial land use, and transportation network have the major roles in the formation and expansion of the SUHI effect in Tehran. The SUHI of Tehran, like that of other arid or semi-arid cities, does not exhibit the classical pattern of SUHI: that is, the hot spots usually are not found in the downtown, as occurs in humid climates. Rather, the SUHI tends to situate over desert areas or barren lands that surround these cities. Therefore, an inversion of the standard SUHI phenomenon during daytime has been observed in Tehran. Research conducted in arid and semi-arid cities suggests that we should refine our point of view on the concept of the UHI in such cities and consider this issue in future studies.

An unprecedented deadly and destructive EF4 tornado struck the Czech Republic across Břeclav and Hodonín districts on June 24, 2021. On this day, several supercells developed in Central Europe, however, in Austria and the Czech Republic region only one cell produced a tornado. For this reason, in addition to the macrosynoptic setup, it is also worth exploring the small-scale cell interactions that can lead to the formation of a devastating EF4 tornado. We use ECMWF analysis and forecast fields, sounding profiles, and radar measurements to examine the synoptic weather situation and convective processes. Moreover, to investigate the evolution and structure of convection, two Weather Research and Forecasting (WRF) model simulations were carried out at 1.5 km grid spacing with one-moment and two-moment microphysical parameterizations. WRF captures the overall spatial distribution and supercellular nature of thunderstorms, although discrepancies exist in the magnitude and spatial location of individual cells. The low-reflectivity region accompanying the thunderstorms is better represented by the one-moment microphysics scheme.

Meteorologists keep searching and running models to provide the most accurate forecast of wind speed in addition to gaining a more detailed understanding of the wind conditions in Hungary. Wind speed and wind energy estimates, forecasts, and their verification are based on wind statistics from a longer or shorter previous period. Consequently, in addition to dynamic methods, purely statistical models also play an important role, i.e., findings that can be obtained from the statistical analysis of the existing database of measured data. The successive phases of the statistical method for producing scientific or operational information that can be extracted from measured, corrected, and stored meteorological data are generally: statistical analysis/processing, creating, verification, and application of the model, recording of the required information. The targeted information in this paper is the daily average of hourly wind speeds. The exact average of this time series can only be determined after the last measurement. To estimate this average during the day, however, the so-called sliding average model has been developed, which can be applied to any climatic element if its measured values are recorded at regular times over a certain period of time. The results presented in this paper are recommended for the preparation of the so-called "timetable", which is one of the most difficult problems for wind farm operators. This is basically the estimation of the amount of electricity produced the following day over short periods. It would be a significant help in the above if we can determine the probability of a decrease or increase in the average wind speed on the next day (and with it, the average daily wind power), or which of these two probabilities is greater. This requires an estimate of average wind speed of the next day. In addition, the results of one of our previous studies on the statistical structure of day-to-day changes in average daily wind speeds were also used. According to the results of the monthly testing of the model over a given period, the frequency of good estimates is between 80.6% and 54.8%.

The climate around the world including Saudi Arabia has been fluctuating from cold to warm during different periods. The climate of the earlier period of the 650 ka BP was warmer than the present time in Saudi Arabia due to greenhouse gases in the atmosphere. The current climate of Saudi Arabia is arid to semi-arid with different climate classes. The seasonal surface air temperatures (SATs) are high in the central and northern regions compared to the southern region. The summer of Saudi Arabia is the warmest around the globe with the exception of the coastal region. Due to different air masses that invade the regions of Saudi Arabia, there are different SATs in different seasons. Depending upon seasonal and annual basis, the frequency of the extreme cold SAT is less than the extreme warm SAT.
The circulation pattern of high and low pressures plays an important role in the climatic SAT of Saudi Arabia. The coldest year is associated with the Siberian high-pressure during winter and early spring, especially in the central and northern areas, while the warmest year is related to the Indian monsoon low-pressure during summer and early autumn especially on the northeastern parts, majority of the east coast, and central regions of Saudi Arabia. On the other hand, the Icelandic low pressure extended to the southern region causes cooling air over the area, especially, the northern part of Saudi Arabia, while the Sudan low-pressure causes warming and moisture from the southern and southwestern regions in the winter season. The synoptic situation in the spring season is almost similar to the autumn season. During the spring and autumn seasons, the synoptic circulation over Saudi Arabia is Siberian high-pressure from the east, subtropical high-pressure from the west, Mediterranean depression from the north, and Sudan low and/or Asian monsoon low from south.

Foehn is a well-known example of local atmospheric circulation and is an extreme weather event for wind gusts. It can cause rapid snowmelt in spring or spread of forest fire in summer, as well as significant economic losses. The foehn in Bulgaria is observed on the northern slopes of the mountains.  For the period 1979–2014, 261 foehn days are registered north of the Vitosha mountain, where Sofia valley is located. The average annual number of foehn days is 8.1, 8.3, and 4.5 for the periods 1985–1994, 1995–2004, and 2005–2014. After 2004, the average annual number of days with foehn decreases, and the lowest maximum wind gusts are registered. To check whether atmospheric circulation changes, could be the reason for this change two objective circulation classifications and a manual one are used to study the foehn occurrence in the Sofia valley. Based on the GrossWettertypen (GWT) catalogue of circulation patterns produced by the COST Action 733 and ten circulation types of the Jenkinson-Collison (JCT) catalogue of weather types the largest number of foehn days occur at the SW, W, and NW circulation patterns. GWT and JCT classifications with 26 types confirm the foehn occurrence during the W and SW flows, but add two more cyclonic types, the CW and CSW. For the foehn days in March 1979–2014, best agreement was found with the manual circulation classification with 26 types. A comparison between the decades 1995–2004 and 2005–2014 shows a substantial decrease in western and northwestern circulation types during the foehn days. An analysis of circulation types for all days confirms an overall reduction of W and NW circulation types after 2004.

Investigation of river flow volume in different conditions as a function of temperature and rainfall variables can be quite effective in understanding the hydrological and hydro-climatic conditions of the watershed. Multiple linear regression models were applied in estimating river flow in several studies due to their straightforwardness and appropriate interpretation of results. In this study, to overcome the limitations of the multiple linear regression model, the Bayesian quantile regression model was used to estimate the river flow volume as a function of rainfall and temperature, and the results were compared. The data and information used for the Qareh-Sou basin in northern Iran are of substantial environmental and socio-economic importance. Five data series, including spring, summer, autumn, winter, and annual series, were created and used for this study. It was found that the Bayesian quantile regression model has considerable flexibility to model the volume of flow for different quantiles, predominantly upper and lower quantiles, and can be used to model high and low flows. With increasing the values of quantiles, a limited decreasing pattern in the effect of rainfall on the volume of flow was identified, which can be due to increasing the effect of other factors in the formation of extreme flows of the river. For summer data in high quantiles, the effect of rainfall on river flow volume shows an increasing pattern. This pattern is different from the other studied series, which may be due to the low base flow in summer. The results confirm that the application of Bayesian quantile regression compared to multiple linear regression leads to much more valuable information on the impact of rainfall and temperature on river flow volume.