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http://dx.doi.org/10.3741/JKWRA.2021.54.9.731

Monthly temperature forecasting using large-scale climate teleconnections and multiple regression models  

Kim, Chul-Gyum (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Jeongwoo (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Jeong Eun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Nam Won (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Hyeonjun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korea Water Resources Association / v.54, no.9, 2021 , pp. 731-745 More about this Journal
Abstract
In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.
Keywords
Climate index; Teleconnection; Monthly temperature; Multiple regression model;
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