برآورد بارش بهاره ایران از طریق تابش موج بلند خروجی زمین (با تأکید بر شمال غرب ایران)

The rains that often occur in the spring season and in the mountainous areas of the northwest of the country cause floods and destroy the environment and human infrastructure. Earth outgoing long-wave radiation is studied as a significant parameter to detect clouds and estimate this type of precipitation. The current study aims to examine the relationship and analysis of outgoing long-wave radiation variables and precipitation values in Arc GIS software environment for 17 statistical years for Iran using AIRS sensor products of Aqua satellite and GPM satellite. Correlation and regression models and confidence interval estimation were used to measure the correlation of outgoing long-wave radiation in predicting precipitation patterns and their changes. Regarding the results obtained in April and May in the northwest of the country, negative correlations above 60% were observed, which may be due to a cloudy factor, In June except in areas in the northwest and southeast of Iran, which indicate a negative correlation between precipitation and outgoing long-wave radiation, other strong negative correlations in other parts of the country were detected due to moisture trapped in the Earth's atmosphere and the lack of rising and instability due to subtropical high pressure, which reduces the radiation of the earth outgoing long-wave radiation. Consequently, using confidence interval maps, the OLR variable can be used to estimate convective precipitation in April in most parts of the country, in May for the northwest, and in June for high latitudes in the northwest and southeast of the country. in other areas, this inverse relationship cannot be used to predict precipitation.

Extended Abstract
 
Introduction
One of the reasons for the variability of rainfall in Iran is the presence of unique geographical and topographical conditions in the country. This creates a suitable environment for the formation of convective precipitation. Considering the role of convective rainfall in damaging natural and human infrastructures, studying these types of rainfall has attracted significant attention from researchers in recent years. It holds special importance in policymaking and planning across various sectors such as agriculture, water management, urban planning, construction, and transportation.Several important factors influence the occurrence of convective precipitation. One of these factors, which is an effective parameter for identifying and forecasting this type of rainfall, is the Outgoing Longwave Radiation (OLR) emitted by the Earth. Long-term measurement of Earth's outgoing longwave radiation is essential for quantitative understanding of climatic conditions. This variable is studied as an important parameter for cloud identification and rainfall estimation. High values of outgoing longwave radiation indicate cloud-free areas and thus warmer land surfaces, whereas cloudy regions have low outgoing longwave radiation, which can be detected by sensors. Therefore, the inverse relationship between outgoing longwave radiation and rainfall can be used to identify areas of convective precipitation.
 
Methodology
Rainfall events, which mostly occur during the spring season in the mountainous regions of northwestern Iran, often cause floods that lead to the destruction of the environment and human infrastructures. Outgoing Longwave Radiation (OLR) is studied as an important parameter for cloud identification and estimation of this type of precipitation.The aim of the present research is to analyze the relationship between terrestrial outgoing longwave radiation variables and precipitation amounts using AIRS sensor products from the Aqua satellite and data from the GPM satellite. This analysis is conducted statistically over 17 years for Iran within the ArcGIS software environment. Correlation and regression models, along with confidence level estimations, were employed to investigate the relationship of outgoing longwave radiation in predicting rainfall patterns and their variations.
 
Results and Discussion
According to the obtained results, in April and May, negative correlations above 60% were observed in northwestern Iran, which can be attributed to cloud cover. In June, except for areas in the northwest and southeast of Iran that show a negative correlation between precipitation and outgoing longwave radiation (OLR), other regions of the country exhibited strong negative correlations due to moisture trapped in the atmosphere and the absence of upward movement and instability caused by the subtropical high-pressure system. This leads to a reduction in outgoing longwave radiation, but no actual precipitation occurs.Therefore, using confidence level maps, the OLR variable can be utilized to estimate convective rainfall in most parts of the country during April, in the northwestern region in May, and in some high-latitude areas in the northwest and southeast in June. However, in other regions, this inverse relationship cannot be used for rainfall prediction.
 
Conclusion
Based on this research, it can be concluded that in April, in the northwestern regions and mountain slopes where orographic convection occurs, cloud cover is a major factor explaining the high negative correlations exceeding 60%. In areas where precipitation systems originate from outside the country, the negative correlations tend to be lower. In May, most of the rainfall in northwestern Iran, the northeastern mountains, and the central mountains is convective in nature, showing similarly high negative correlations.In June, except for small areas in the northwest and southeast of Iran that exhibit a negative correlation between precipitation and outgoing longwave radiation (OLR), other regions show strong negative correlations caused by moisture trapped in the atmosphere and the lack of upward motion and instability due to the presence of the subtropical high-pressure system. This leads to a reduction in OLR, but no actual precipitation occurs.According to the correlation maps, a negative relationship between daily outgoing longwave radiation and precipitation was observed on monthly and seasonal timescales. This inverse relationship was statistically significant at the 0.05 error level and 95% confidence, confirmed by the significance test of correlation (t-test).Therefore, using confidence level maps, the OLR variable can be applied to estimate convective rainfall in most parts of the country during April, in the northwest in May, and in some northern latitudes in the northwest and southeast in June. However, in other regions, this inverse relationship cannot be used for precipitation prediction.