Document Type : Research Paper
Authors
1 Ferdowsi University of Mashhad
2 Department of Natural Disasters and Climate Change, Faculty of Climatological Research Institute, Mashhad, Iran
3 Zabol University, Zabol, Iran
Abstract
Teleconnection patterns are one of the causes of precipitation fluctuations in various regions of the world, including Iran. These patterns can be used as predictors in precipitation forecasting models. This study aims to develop the bivariate models for forecasting autumn precipitation in the northwestern region of Iran based on teleconnection indices. Copula functions were chosen for this task because the assumption of normal distribution for precipitation data is not met (making Pearson correlation unsuitable), and because of the nonlinear relationship between precipitation and the teleconnection indices. 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 windows of one to six months. Appropriate copulas were then used to model precipitation, and the performance of the developed models was evaluated. The results showed the highest Kendall's Tau 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 the years 2021 and 2022, with values of -4.2 mm and -5.8 mm, respectively.
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