ارزیابی داده‌های هواشناسی و تصاویر ماهواره‌ای در شناسایی پدیده گردوغبار مناطق بیابانی (مطالعه موردی: استان کرمان) | Evaluation of meteorological data and satellite images in identifying dust phenomenon in desert areas (Case study: Kerman province)

Persian. توفان گردوغبار به یک معضل جدی در ایران تبدیل شده است و روزبه‌روز بر دامنه این معضل افزوده می­شود. به‌منظور ارزیابی داده­های هواشناسی و تصاویر ماهواره­ای در آشکارسازی بررسی وضعیت رخداد پدیده گردوغبار در استان کرمان، داده‌های ایستگاه‌های سیزده ایستگاه سینوپتیک استان برای یک دوره 20 ساله از سال 1379 تا 1399 هجری شمسی مختلف از سازمان هواشناسی کشور (IRIMO) دریافت شد و تصاویر سنجنده مودیس (MODIS) مورداستفاده قرار گرفت. به‌منظور آشکارسازی گردوغبار کدهای 06، 07 و الگوریتم­های Ackerman، TDI، TIIDI، Roskovensky and Liou,NDDI و Miller استفاده شد. نتایج نشان داد ایستگاه سیرجان بیشترین وقوع گردوغبار با منشأ محلی و منطقه‌ای را دارد و گلباف کمترین وقوع رخداد را دارد. ایستگاه بم بیشترین فراوانی سالانه را داراست و کمترین وقوع، شهر بابک با 2 روز گردوغباری است. از میان الگوریتم‌های موردبررسی الگوریتم­های TIIDI و  TDIدارای عملکرد مناسب­تری در میان سایر الگوریتم­ها است. درمجموع بررسی داده­های فراوانی سالانه دارای دید افقی 1000 متر و کمتر بیانگر روند افزایشی وقوع طوفان‌های گردوغبار از سال 1379 تا 1390 است و بعدازآن تا سال 1399 روند کاهشی را طی کرده است.

English. Extended Abstract Introduction Dust storms have become a serious problem in Iran, and the scope of this issue is increasing steadily. The country of Iran, due to its geographical location and climatic conditions, is particularly susceptible to dust storms. Dust storms and the spread of haze in various regions of the country have been some of the most significant environmental challenges in recent years, affecting not only Iran but also the Middle East and Western Asia. These challenges have had adverse social, economic, environmental, and commercial effects on the people of this region, severely disrupting their daily lives. Additionally, dust is considered a significant source of heavy metals in the environment. The aim of the present research is to examine meteorological codes and evaluate the performance of detection algorithms in identifying dust storms in Kerman Province. This study aims to provide a better understanding of dust phenomena in this province and assesses the accuracy and efficiency of various algorithms in detecting dust storms. Furthermore, it identifies the most effective algorithm for this purpose. Material and Methods Kerman Province, with an area of 182,301 km², is located in the southern part of Iran between 53°26' to 59°29' east longitude and 25°55' to 32° north latitude. It is the largest province in terms of area. The northern boundary of the province is bordered by the provinces of Khorasan and Yazd, the southern boundary by Hormozgan Province, the eastern boundary by Sistan and Baluchestan Province, and the western boundary by Fars Province. The average annual rainfall in the province is 145 millimeters, which is approximately 58% of the national average and 19% of the global average rainfall. To evaluate meteorological data and satellite images in detecting dust phenomena in Kerman Province, data from thirteen synoptic stations covering a period of 20 years (2000 to 2020) were obtained from IRIMO. MODIS images were used to analyze the dust phenomenon. To detect dust, codes 06 and 07 were applied, along with algorithms such as Ackerman, TDI, TIIDI, Roskovensky and Liou, NDDI, and Miller.   Results and Discussion Dust storms are common phenomena in arid and semi-arid regions of the world. In recent years, the occurrence of frequent and intense dust storms has become one of the most destructive environmental disasters in the Middle East, with Kerman Province being one of the regions most severely affected. This province has been given particular focus in this research. The results showed that Sirjan station has the highest occurrence of dust storms with both local and regional origins, while Golbaf station has the lowest occurrence. Bam station recorded the highest annual frequency, while the least occurrence was observed in Babak city, with only two days of dust per year. Among the studied algorithms, the TIIDI and TDI algorithms demonstrated better performance compared to others. Overall, the annual frequency data with a horizontal visibility of 1,000 meters or less indicates an increasing trend in dust storm occurrence from 2000 to 2011, followed by a decline until 2020. Conclusion Although the Ackerman algorithm showed relatively acceptable performance in detecting dust, it performed poorly in northern regions and near dust source areas. Studies on other algorithms indicate inadequate performance in detecting dust phenomena in the studied area. Therefore, it is recommended that, considering the specific conditions of Kerman Province, serious attention should be given to identifying dust source areas and planning strategies to control and mitigate the negative effects of dust storms. This is essential for future research projects to prevent potential damage to industrial and agricultural facilities and other vital infrastructure.