This study investigates the impact of the OLR MJO Index (OMI) and the Real-time Multivariate MJO (RMM) of the Madden-Julian Oscillation on the frequency of dust storms in the stations of Abadan, Ahvaz, Bostan, Bandar Mahshahr, Dezful, Ramhormoz, and Masjed Soleiman, located in Khuzestan province, during the period from April to September 1987-2021. Pearson correlation coefficients were computed to assess the relationship between dust data and the indices, and the findings were depicted through zoning maps. Subsequently, the frequency percentage of each index for both positive and negative phases was quantified. The results indicate a direct and significant correlation between the positive and negative phases of the indices and dust occurrences (with the exception of Dezful station), particularly during the positive phase of the OMI and the negative phase of the RMM. The highest correlation coefficients, ranging from 0.77 to 0.72, were observed for Bandar Mahshahr and Dezful stationsduring the positive phase of the RMM index. Analysis of the relationship between the Madden-Julian Oscillation and dust storms revealed that between 51% and 59% of dust storms in Khuzestan province occurred in the negative phase of the OMI index, while 40% to 49% occurred in the positive phase. In the case of the RMM index, 56% to 63% of dust storms were associated with its negative phase, in contrast to 37% to 50% linked to its positive phase. Notably, the negative phase of the RMM index exhibited a higher percentage of dust storms compared to the negative phase of the OMI index. According to the results of the Monte Carlo test, the displacement of the positive and negative phases of the RMM index significantly contributes to the occurrence of dust storms at most stations in Khuzestan province. Furthermore, tracking the pathways of dust entering Khuzestan province using the HYSPLIT model indicates the movement of particles originating from Iraq, Arabia, and the eastern regions of Syria toward Khuzestan province..

Drought is a natural hazard that annually causes significant economic, social, environmental, and life-threatening damage in vast areas of the Earth. The damages caused by this phenomenon are intangible but very extensive and costly.  In many circomstancs, modern remote sensing techniques can be a useful tool in monitoring drought due to high temporal accuracy, wide spectral coverage, ease of access, no need for atmospheric correction and ground referencing. In recent years, the province of Hamedan has faced many problems due to frequent droughts. Therefore, the present study focused on investigating and monitoring drought in Hamedan province using the Temperature Condition index and its impact on the vegetation cover of the province using Advanced Very High Resolution Radiometer (AVHRR) and National Oceanic and Atmospheric Administration (NOAA) remote sensing data. First, the relevant data was extracted from the Nova star database, and finally, the spatiotemporal behavior of the vegetation cover drought index was examined on 1528 pixels in Hamedan province. The spatial resolution of the data used in this study is 4 kilometers. First, the relevant data were extracted from the Navstar database and ultimately, the spatiotemporal behavior of the drought index and vegetation cover was examined. The results indicate that drought has significantly increased the vegetation cover of Hamedan province based on remote sensing data. Kendall's coefficients indicate the presence of decreasing trends in vegetation cover at a 95 Percent confidence level. Only in May, June, and December has there been a slight decrease in vegetation cover within the extent of drought in the province. The spatial behavior analysis of the drought index on vegetation cover showed that February, March, as well as April have experienced more severe droughts within Hamedan province.

Understanding the daily weather types of any specific location is crucial for identifying its long-term climate patterns. In this study, we utilized the Wos classification method in conjunction with a comprehensive climatological approach to analyze key variables, including minimum, average, and maximum temperatures, as well as cloud cover and daily precipitation. Data from 1985 to 2021 were collected from 39 synoptic stations, which exhibited a well-distributed representation across the country and provided complete datasets. Weather types were identified using established coding techniques. The findings indicated that the predominant temperature types in the country are primarily categorized as hot and very hot, with sub-codes reflecting generally low to moderate cloud cover and negligible precipitation. Furthermore, the application of Ward's clustering method facilitated the identification of three distinct climatic groups. The geographical characteristics of each location, including factors such as altitude, latitude, proximity to the sea, and synoptic influences, play a significant role in the regional differentiation of these groups within the country. The outcomes of this research can be instrumental in developing weather calendars for various regions, with implications for numerous sectors including agriculture and tourism.
 

Extratropical cyclones, characterized by their frequency, duration, and intensity, serve as the primary drivers of mid- and high-latitude precipitation across the Mediterranean during the winter and autumn months. For this research, climatic variables obtained from the ECMWF network, featuring a temporal resolution of 6 hours and a spatial resolution of 0.25° × 0.25°, spanning from 1979 to 2016, were utilized. Additionally, precipitation data from four basin stations sourced from the Asfezari database for the same period were analyzed. Initially, geopotential height, temperature, humidity, and jet stream data for rainy days were extracted using MATLAB. Subsequently, a cyclonic center extraction algorithm was applied to identify cyclonic centers from the geopotential height data, based on the conditions that the geopotential height is at a minimum and the geopotential gradient is at a maximum. From the geopotential height matrix of rainy days (361×441×498), four distinct atmospheric patterns were identified through cluster analysis. The temporal and spatial frequency of these patterns, as well as the average temperature of cyclonic centers, were analyzed for the cold season months. The results indicated that the first pattern, identified as the Mediterranean trough pattern, is the most frequent, occurring 42% of the time. This pattern is characterized by the presence of a high-level system acting as a barrier, which deepens the low-level Mediterranean system and extends its axis toward the Red Sea. The interaction between low-level and high-level systems enhances instability, resulting in the highest precipitation levels among the identified patterns. Conversely, the fourth pattern, termed the western wind trough pattern, exhibits the lowest frequency at 10%. This pattern is characterized by a trough over the Caspian Sea; however, a high-level system in the southern region inhibits the entry of low-level systems, thereby confining cyclonic activity to the northern portion of the study area. Consequently, the isobars in the northern region assume a more orbital configuration, leading to a decreased influx of cyclones and, as a result, lower precipitation amounts compared to the other patterns. The analysis further revealed that cold-core cyclones accounted for 60% of occurrences in winter and 40% in autumn, while hot-core cyclones constituted 62% in winter and 38% in autumn. Notably, the frequency of hot-core cyclones increased relative to cold-core cyclones in winter, whereas an inverse trend was observed in autumn. Over the past decade, both the frequency and intensity of cyclones have diminished compared to the preceding two decades. In terms of cyclogenesis locations, the western part of the study area has consistently emerged as the most active region. Moreover, cyclogenesis activity exhibits a gradual increase from autumn to winter as the cold season progresses. These findings underscore the dynamic nature of extratropical cyclones and their significant role in shaping precipitation patterns across the Mediterranean region.
 

Dust is a phenomenon with significant environmental impacts across various aspects of human life, including agriculture, economy, health, and more. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a 3-hour resolution for the statistical period (2000–2020) from the Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized, and then Artificial Neural Network (ANN) models were used to predict dust concentration, while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to analyze and predict the time series of dust occurrence in MATLAB software. The findings revealed that the maximum predicted dust concentration, related to the minimum dew point with the highest Pearson correlation with dust, was estimated at 3451.23 µg/m³. Additionally, the results of the time series prediction using the ANFIS model showed that the linear bell membership function with grade 3, during both the training and testing stages, was the most effective input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah is 94%. Based on the aforementioned studies, sufficient information was gathered to conduct this research. The phenomenon of dust, particularly in western Iran and the city of Kermanshah, has consistently posed significant challenges for the residents of these areas. This phenomenon is influenced by specific atmospheric conditions that cause irreparable damage annually, leading to respiratory issues and deteriorating air quality. Therefore, it is essential to pay serious attention to the issue of dust.
 

To assess thermal comfort conditions in classrooms, a field study was conducted in Sabzevar. The thermal sensations reported by students regarding classroom conditions were documented at various times throughout the day during the 2009–2010 academic year across multiple classes. temperature and humidity data within the classrooms were recorded simultaneous using a data logger. To analyze differences, both ANOVA and the Kruskal-Wallis test were employed. The findings indicated that the geographical orientation and floor level of the classrooms did not significantly influence temperature and humidity levels. In contrast, significant hourly variations in these parameters were observed. Overall, reports of cooling sensations were more prevalent than those of heating sensations (24% vs. 12%). Thermal sensation exhibited considerable variation across different months, with October recording the lowest frequency of thermal comfort sensations. In all months except October, students expressed a preference for "heating." Although the performance of the heating system was deemed adequate, its operational schedule should be modified to commence closer to the beginning of morning classes in order to mitigate substantial energy waste. While temperature and humidity within the classrooms did not present significant monthly variations, students' thermal sensations varied markedly between months. This suggests that thermal sensation is influenced by factors beyond mere physical characteristics (temperature and humidity). In addition to climatic parameters, individual characteristics such as sex, age, weight, height, clothing, and activity level also play a significant role in shaping perceptions of thermal comfort. 

The torrential rains that occurred in April 2017 in Lorestan Province exemplified severe precipitation that inflicted substantial damage on agricultural, urban, transportation, and communication infrastructures. This study aims to investigate and elucidate the relationship between the physical structure of clouds responsible for two waves of heavy rainfall in April 2017 within the Doroud catchment area of Boroujerd. In this context, the statistical characteristics of two precipitation events on March 25 and April 1, 2019, were analyzed. The microphysical properties of the clouds generating these two heavy rainfall events were examined utilizing the Madis superconductor product and MOD06. Four microphysical factors contributing to the formation of clouds during these two rainfall waves in the Doroud-Borujerd basin—including cloud top temperature (CTT), cloud top pressure (CTP), optical cloud thickness (OCT), and cloud cover ratio (CFR)—were analyzed. Statistical assessments indicated that the first wave of heavy rainfall, occurring on March 25, 2019 (5 April 1398), accounted for 15% of the total annual rainfall, while the second wave on April 1, 2019 (12 April 1398) contributed 20% of the region's average annual rainfall within these two days. The findings from the analysis of the microphysical structure of the clouds producing these two precipitation waves, based on data from the MODIS cloud sensor product, revealed a significant spatial correlation between the four microphysical factors and the recorded precipitation values of these two heavy rainfall events. Specifically, the cloud top temperature and pressure, indicative of vertical cloud expansion in the area, exhibited a significant inverse relationship with the precipitation amounts in the basin. Conversely, the cloud cover ratio and optical thickness demonstrated a direct and significant spatial correlation with the recorded rainfall values. The results of this study thus establish a significant and robust relationship between the microphysical structure of clouds and the precipitation amounts recorded in the region during these two heavy rainfall events. 

Considering that urban land-use change in metropolises such as Tehran has been increasing in recent decades; Therefore, the formation of the thermal island phenomenon in the city can be studied as one of the environmental problems. Increasing construction, density, and building heights will change the complexes' geometry and shape, followed by changes in climatic conditions and micro-urban climates. Therefore, in this study, we tried to investigate urban geometry's effect on forming a thermal island in Tehran. The study region in this research includes regions one, two, and three of Tehran.
The methods used in this research included (1) Oak's numerical equation and algorithm design to simulate the intensity of the heat island. In the first stage, digital GIS data including building blocks (parcels) in polygon format and street widths, which were prepared and prepared by Tehran Municipality based on the 2016 detailed audit plan, were used. (2) Modeling was performed in Envi-met software to study the effect of city geometry on wind. The results of the studies showed that the two factors of building height and street width (ratio (H/W)) play an important role as two key factors in studying urban geometry; therefore, in studying the intensity of the heat island, the factor of building height and high-rise construction can play an important role in the formation of the heat island. However, the higher the height of the building compared to the width of the streets, the more it acts as a barrier against the heat island. The intensity of the heat island adjusted with the Oak equation showed that the factor of building roughness coefficient can be an important factor in adjusting the intensity of the heat island. Spatial analysis of images and outputs of the Envi.met model showed that the two main factors of construction density And the height of the building has a greater impact on wind speed transmission than the slope and topography of the area. Therefore, in the study of urban design for future studies, appropriate planning and proper management of resources are needed for the climatic comfort of residents, which can be designed to be beautiful and safe cities by considering the principles of architectural safety.