نوع مقاله : مطالعه موردی

نویسندگان

1 استاد، گروه آمار، دانشکده ریاضی، دانشگاه فردوسی مشهد، مشهد، ایران

2 استادیار، پژوهشگاه هواشناسی و علوم جو، پژوهشکده اقلیم شناسی و تغییر اقلیم، مشهد، ایران

3 استادیار، گروه آمار، دانشکده علوم پایه، دانشگاه زابل، زابل، ایران

چکیده

الگوهای پیوند از دور، از علل نوسان‌های بارش مناطق مختلف دنیا از جمله ایران به شمار می‌روند. از این متغیرها می‌توان به‌عنوان تخمین­گر در مدل‌های پیش‌بینی بارش استفاده کرد. هدف این پژوهش، ارائه مدل‌ دو متغیره پیش‌بینی بارش پاییزه بر مبنای این الگوها برای منطقه شمال غرب کشور می‌باشد. به‌دلیل عدم تحقق فرض نرمال بودن بارش و در نتیجه عدم امکان استفاده از ضریب همبستگی پیرسون و همچنین وجود رابطه غیر­خطی بین این نمایه‌ها و بارش، در این پژوهش، از توابع مفصل استفاده شد. وابستگی دورپیوندهای اقیانوس آرام و اقیانوس اطلس با بارش برای دوره 2020-1991 با استفاده از ضریب وابستگی رتبه‌ای کندال و ضریب وابستگی رتبه‌ای پیرسون برای میانگین متحرک 1 تا 6 ماهه محاسبه شد. مفصل‌ها و توزیع‌های کناری مناسب برای مدلسازی ناهنجاری بارش بکار رفت و کارایی مدل‌های تدوین شده بررسی شد. نتایج نشان داد بالاترین ضریب وابستگی رتبه‌ای کندال، بین ناهنجاری بارش و دورپیوندهای NINO3.4، SOI و MEI بدست آمد. در نتیجه، مدل‌های تدوین شده با این نمایه‌ها، دارای کارایی بالاتری در شبیه‌سازی بارش بودند. در این بین، مدل دارای پیشگوی NINO3.4، بهترین برآورد ناهنجاری بارش را برای سال 2021 و 2022 به‌ترتیب به میزان mm 4/2- و 5/8- ارائه داد. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluating the Utility of Bivariate Copula-Statistical Models for Forecasting Autumn Precipitation (Case study: Northwest of Iran)

نویسندگان [English]

  • Mohammad Amini 1
  • Mansoureh Kouhi 2
  • Morteza Mohammadi 3

1 Professor, Department of Statistic, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

2 Assist. Professor, Department of Applied Climatology, Faculty of Climate Research Institute, Research Institute of Meteorology and Atmospheric Sciences (RIMAS), Mashhad, Iran

3 Assist. Professor, Department of Statistics, Faculty of Basic Sciences, University of Zabol, Zabol, Iran

چکیده [English]

Teleconnection patterns are one of the causes of precipitation fluctuations in various regions of the world, including Iran. This study aims to develop bivariate models for forecasting autumn precipitation in the north-western region of Iran based on teleconnection indices. Copula functions were selected for this assignment due to the nonlinear relationship between precipitation and the teleconnection indices, as well as the fact that the assumption of normal distribution for precipitation data is not met, rendering Pearson correlation inappropriate. The dependence of Pacific and Atlantic Ocean teleconnection indices with precipitation for the period 1991-2020 was calculated using Kendall's and Pearson's rank correlation coefficients for moving average of one to six months. Appropriate copulas and marginal distributions were then used to model precipitation, and the performance of the developed models was evaluated. The results showed that the strongest correlations were obtained between the precipitation and the NINO3.4, SOI, and MEI indices. Consequently, the bivariate models using these indices demonstrated higher efficiency in simulating precipitation anomalies. Among these models, the one with the NINO3.4 predictor provided the best estimate of precipitation anomaly for 2021 and 2022, with values of -4.2 mm and -5.8 mm, respectively.

کلیدواژه‌ها [English]

  • Anomaly
  • Bivariate Models
  • Copula
  • Predictor
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