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Enhancing Subseasonal Temperature Prediction by Bridging a Statistical Model With Dynamical Arctic Oscillation Forecasting
Author(s) -
Kim Minju,
Yoo Changhyun,
Choi Jung
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2021gl093447
Subject(s) - forecast skill , arctic oscillation , bridging (networking) , northern hemisphere , ensemble forecasting , environmental science , climatology , statistical model , oscillation (cell signaling) , meteorology , data assimilation , statistical ensemble , surface air temperature , global forecast system , computer science , numerical weather prediction , statistics , mathematics , geology , physics , precipitation , computer network , canonical ensemble , biology , monte carlo method , genetics
This study proposes a hybrid approach to improving subseasonal prediction skills by bridging a conventional statistical model and a dynamical ensemble forecast system. Based on the perfect prognosis method, the phase of the Arctic Oscillation (AO) from the European Centre for Medium‐range Weather Forecasts ensemble forecast system is used as a predictor in a composite based statistical model to predict the wintertime surface air temperature in the Northern Hemisphere. The hybrid model, which employs AO phases predicted by the dynamical model for weeks 1–4, generally outperforms the conventional statistical model for lead times of weeks 2–6. The improved skill score is due to the high accuracy of the AO forecast from the dynamical model and the strong lagged connection between the AO and temperature. This study thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting.