ارزيابي نقش تغييرات سيگنال‌‌هاي اقليمي برنوسان‌‌هاي بارش بهاره تحقيق موردي: استان خراسان رضوي

The assessment of the role of climatic signal changes on spring rainfall oscillations, Case study: Khorasan Razavi Province

به‌‌منظور بررسي ارتباط بين سيگنال‌هاي بزرگ‌‌مقياس اقليمي با بارش بهاره‌، استان خراسان رضوي انتخاب شد. پس از بررسي‌‌هاي لازم و تكميل خلاهاي آماري موجود براي ايستگاه‌‌هايي كه كمتر از 5 سال خلا آماري داشتند، تعداد 38 ايستگاه همديدي، اقليم‌‌شناسي و باران‌‌سنجي تحت پوشش سازمان هواشناسي كشور و وزارت نيرو، انتخاب و آزمون همگني موسوم به «ران تست» روي داده‌‌هاي بارش اين ايستگاه‌‌ها صورت گرفت. در مرحله بعد، در محيط نرم‌‌افزار Arc GIS، سري زماني بارش متوسط منطقه‌‌اي به روش كريجينگ محاسبه شد. با استفاده از روابط همبستگي به‌‌دست آمده بين بارش متوسط منطقه‌‌اي و سيگنال‌‌هاي هواشناسي شاخص در بازه‌‌هاي زماني گوناگون، سيگنال‌‌هاي اقليمي موثر بر آب‌‌وهواي استان خراسان رضوي مشخص شد كه از اين سيگنال ها مي‌‌توان در مدل‌‌هاي پيش‌‌بيني بارش و همچنين تشخيص دوره‌‌هاي خشك، تر و نرمال استفاده كرد.

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to constructing computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, we analyzed 38 years of rainfall data in Khorasan-e Razavi province, the northeastern part of Iran. Total precipitation from April to June over a period of 38 yeas (1970-2007) was selected as data of our interest in this research and the RUN TEST homogeneity was performed to find out if the rainfall data were randomly collected. Data of 38 stations including 4 synoptic, 10 climatology and 24 rain gauge stations (all belong to Iranian Meteorological Organization and Ministry of Energy) were selected for each year. Calculation of local average rainfall: The Kriging method is used to estimate the amount of local average rainfall. Data: The data used in this study are: 1) 38 Rainfall station data for the seasonal rainfall (Apr – Jun), a All of these stations are in the northeastern region of Iran. 2) Large-scale ocean and atmospheric circulation variables such as Sea Level Pressure (SLP) and the Sea Level Pressure difference in pre-rainfall months (Oct – March). These data were obtained from NCEP/NCAR Re-analysis data. These data sets span the period of 1948 – current, covering the globe on a 2.5*2.5 ?grid and available at http://www.cdc.noaa.gov NOAA website. Identification of Predictors: The aim in this section is to identify predictors for seasonal rainfall, which can then be used in forecast models. The two main requirements for any useful predictors are: (i) good relationship with the seasonal rainfall, (ii) reasonable lead-time (i.e. months to season). Our earlier work indicated that seasonal rainfall in the region is strongly correlated with predictors. So, the first step is to look for relationship with standardized predictors during the pre-rainfall seasons (Oct-March) and follow up with correlations between the rainfall and large-scale ocean-atmospheric variables (SLP, and SLP difference). This approach of correlation with large-scale ocean-atmospheric circulation variables is used to identify predictors for seasonal rainfall in the northeast of Iran Correlation with Large-Scale Variables: We would like to check predictors large-scale aspects and also the seasonal rainfall correlation with predictors such as SLP and SLP difference during pre- season rainfall (Oct-March). In this research, the correlations that are significant at 95% confidence level have been selected. Results showed strong relation between Sea Level Pressure (SLP) and sea Level Pressure difference (?SLP) changes with the rainfall of the studied areas. It can be concluded that meteorological signals may help us to predict the wet and dry seasons.