بررسی پیش بینی پذیری بارش شمال شرق ایران با رویکرد پیوند از دور و استفاده از واکاوی تغییرات الگوهای فشار سطح دریا در مقیاس سیاره ای

بارش هر منطقه به جز ویژگی های محلی، متاثر از عوامل کلان گردش عمومی جو در مقیاس سیاره ای است. جهت بررسی پیش بینی پذیر بودن بارش و شناخت تاثیر تغییرات الگوهای فشار سطح دریا بر بارش یک سال آبی (اکتبر تا سپتامبر سال بعد)، نقشه های همبستگی تغییرات الگوهای فشار سطح دریا در مقیاس جهانی با بارش سالانه شمال شرق کشور (استان خراسان های رضوی، شمالی و جنوبی) و تحلیل رگرسیون چند متغیره در مقیاس 2/5 در 2/5درجه قوسی در دوره 1987 تا 2021 ترسیم و بررسی شد. یافته های پژوهش نشان دهنده وجود همبستگی معنی دار بارش با الگوهای فشار سطح دریا در تاخیر یک تا شش ماهه است. بیشتر کانون های بیشینه همبستگی منفی در اقیانوس آرام و بیشتر کانون های بیشینه همبستگی مثبت در شمال اقیانوس هند، دریای عرب، جنوب شرق آسیا و نواحی حاره ای اقیانوس اطلس قرار دارند. بیشترین همبستگی در تاخیر 1، 2 و6 ماه به ترتیب در خلیج بنگال (0.66)، اقیانوس اطلس (0.63) و امریکای جنوبی(0.66 ) می باشد. هم چنین کانون های بیشینه همبستگی می توانند به نحو مطلوبی بارش سالانه را پیش بینی کنند. عملکرد مدل های برازش داده شده؛ برای تاخیرهای 5 و 6 ماه نسبت به مدل های تاخیر دیگر (تاخیر یک تا چهار ماه) بهتر است، این مدل ها (تاخیر 5 و 6 ماه) به ترتیب 70 و 65 درصد تغییرات بارش را تبیین می کنند. با توجه به تغییرات زیاد بارش سالانه در شمال شرق (دامنه تغییرات بارش 192میلی متر) نتایج این پژوهش می تواند در سیاست گذاری های یک ساله در پیش آگاهی و مدیریت منابع آب در بخش های کشاورزی، صنعت، شرب، منابع طبیعی و زیست محیطی مفید و کمک کننده باشد.

investigating the predictability of precipitation in northeastern iran with a teleconection approach and using the analysis of changes in sea level pressure patterns on a planetary scale

introductionthe atmosphere is a fluid and interconnected system. any change by known and unknown factors in a limited part of the atmosphere can cause larger changes in the general circulation of the atmosphere with the passage of time, this itself causes a change in the water share of each region of the water cycle.the researches that have been carried out in order to predict rainfall in different areas of iran have mostly focused on atmospheric indicators, which were initially made to predict rainfall or study climate changes in areas other than iran. in fact, in these researches, it shows the effect of special atmospheric circulation patterns, such as walker, north atlantic oscillation, on rainfall of iran. on the other hand, pressure changes in limited areas have been analyzed to find a suitable predictor (index) for seasonal rainfall of iran. it is necessary to carry out complete and comprehensive research (sea level pressure patterns on a planetary scale) to investigate the relationship between sea level pressure changes and precipitation in different climatic regions of iran.materials and methodsthe study area is considered for research and investigation of the explanation of the changes in the time series of precipitation in the annual time scales by the spatial changes of the sea level pressure in the time lags of one to six months, in the northeastern region of iran (khorasan razavi, northern khorasan, and southern khorasan provinces).precipitation data for the 35 year statistical period (1987 2021) on a daily basis for the selected synoptic stations including of mashhad, sarkhs, qouchan, torbat haidarieh, sabzevar, kashmer, gonabad, birjand, nehbandan, tabas, ferdous, qain, beshravieh and bojnoord was obtained from the iranian meteorological organization.in order to investigate the relationship between changes in sea level pressure and annual rainfall in northeastern iran, monthly data of sea level pressure were used. these data are presented and updated on the website of the national center for atmospheric and oceanographic studies affiliated with the united states of america since 1979. in general, regression and correlation methods are used to determine the presence or absence of a relationship between variables. the correlation index shows the relationship between two variables that are both affected by common factors. both of these variables are random and other unknown factors jointly affect their occurrence. monthly time series of sea level pressure data for the time period from 1987 to 2021 were made for 10512 cells (2.5 by 2.5 degrees of arc). the months of september, august, july, june, may and april respectively correspond to the delays are one to six months. in order to be able to investigate the quantitative and spatial changes of precipitation with sea level pressure in the six months before the onset of precipitation, correlation maps were drawn for each of the six months (time delay of one to six months). in the second step, the significance of the correlation was investigated using inferential statistics (hypothesis testing) and the points whose correlation was significant on the map was drawn in the third step, for the practical use of sea level pressure changes to predict precipitation, the points whose correlation was significant (places with maximum correlation) were selected as predictors (independent variable). using the data of these selected points and multivariable regression, rainfall forecasting models were fitted. the performance of these models in rainfall forecasting was evaluated using indices such as correlation coefficient, explanatory coefficient and root mean square errors. models with the best performance in predicting rainfall with a delay of one to six months were selected.