Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature

Abstract

AbstractDownscaling and simulating various meteorological variables at different time scales are fundamental topics for making climate change studies in a geographic region. Here, a new approach for downscaling the mean daily temperature was implemented using a vine copula‐based approach and considering the best CanESM2 predictors. The accuracy of the copula‐based approach was compared with genetic programming (GP), optimized support vector regression (OSVR), support vector machine (SVM), adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) models at Birjand synoptic station in Iran. In the proposed approach, after examining the different vine copulas, the D‐vine copula was selected as the best copula according to the evaluation statistics and tree sequences. According to the root‐mean‐square error (RMSE) and Nash–Sutcliff efficiency (NSE), the accuracy of the ANN model in downscaling the mean daily temperature data was not acceptable and the other considered models were slightly overestimated. The results indicated that the copula‐based approach outperformed the other models in downscaling the mean daily temperature with NSE = 0.61. However, given the 99% confidence interval of the simulations, a slightly overestimation at temperatures above 20°C was observed for the copula‐based approach, which has better performance than the other considered models. The copula‐based approach was able to reduce RMSE by about 82, 20, 24, 47 and 34% compared to ANN, OSVR, GP, SVM and ANFIS models, respectively. The results also showed that the performance of the support vector regression model optimized by the ant colony algorithm is also acceptable and is in the second rank after the copula‐based approach. The accuracy of the copula‐based approach was also confirmed according to Taylor diagram and violin plot. The proposed approach has a higher accuracy than data‐driven models due to use of the conditional density of vine copulas, and the joint distribution of the mean daily temperature and selected predictors.