To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs.  The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts.  The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.

The performance of broiler chickens is directly influenced by temperature changes and the occurrence of heat stress, whether it is cold or hot. The present study aims to assess the cooling and heating degree days in different stages of broiler chicken production in Khorasan Razavi province. To achieve this objective, daily average temperature data were collected from 13 synoptic stations during the statistical period of 1988-2018. Cooling and heating degree days were calculated for each week of production using specific thresholds, and their spatial distribution was analyzed. Furthermore, the relationship between cooling and heating degree days and geographical features was evaluated. The findings indicate a decrease in cooling degree days and an increase in heating degree days as latitude increases. The northern and western regions of the province exhibited a greater need for heating throughout all stages of broiler chicken production, whereas the eastern and southern regions had higher cooling requirements at different production stages. Overall, the northeastern, southern, and western marginal areas of the province, including Khaf, Gonabad, Kashmar, Sarakhs, and Sabzevar, exhibited the lowest cooling and heating needs during the 6-week period of broiler chicken production, making them suitable climates for this economic activity.

Each plant needs a certain amount of heat at the time of planting and during its growth and development. The purpose of this research is to determine the thermal requirements of grain corn in the stages of growth phenology in the climatic conditions of Moghan and to determine the suitable cultivation areas in terms of heat requirements in northwest Iran. For this purpose, the technology of corn variety Single Cross 704 recorded consecutively from 2011 to 2014 at the Moghan Meteorological Research Farm and agricultural data from 51 synoptic stations in the northwest of the country (1996-2016) selected from the General Meteorological Organization of the Country have been used. To carry out this research, thermal requirements in each of the 5 main phases of phenology have been determined at the Moghan station, then using the regression equation between the 15-day average of temperature and altitude, a temperature-temperature history map for seed cultivation in all stations from the first half of the year was drawn using Geographic Information System software. Also, the length of scientific periods and the degree of growth day were calculated and the relevant maps were drawn using the Kriging method. Then, it was combined with the layers of altitude, slope of the region and land use, and finally, a zoning map of lands suitable for grain corn cultivation in the northwest of the country was drawn. Based on the results obtained, 27.6 percent of the study area is capable of cultivating this plant.

Climate change is a significant environmental concern due to the sensitivity of glacier melting processes and snow density to climate variations. Currently, a range of satellite sensors, including AVHRR, MODIS, GEOS, and MERIS, are employed for snow monitoring and are extensively utilized to analyze fluctuations and changes in global snow cover. The MODIS sensor is particularly favored for its extensive global spatial coverage, suitable spatial accuracy, and frequent temporal coverage across various scales. Consequently, this study utilizes snow products derived from the MODIS sensor. In this investigation, data on snow-related days from the statistical period of 1989 to 2018 were collected for three provinces: Kermanshah, Ilam, and Lorestan. This data was processed using MODIS snow cover information pertinent to the Middle Zagros region, in conjunction with remote sensing techniques. The study provides a detailed examination of snow cover changes within the specified area. The Normalized Difference Snow Index (NDSI) was employed to detect snow cover within the MODIS sensor products. To differentiate pixels and identify various phenomena, the acquired images were processed using Geographic Information Systems (GIS) technology. Analysis of seasonal snow cover changes using MODIS sensor imagery indicates a significant decreasing trend in the majority of the studied area, particularly in the elevated regions. Notably, only the western and southwestern regions of the study area exhibit no discernible decreasing trend. Furthermore, the examination of snow-covered days throughout the study period reveals a decrease in snow cover in the Middle Zagros, with these changes intensifying in recent years, especially in the snow-covered areas of the region. Changes in snow cover during the winter months, particularly in elevated areas, were observed to be more pronounced compared to other seasons and regions within the study area.

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.