Combining Dynamical and Statistical Modeling to Improve the Prediction of Surface Air Temperatures 2 Months in Advance: A Hybrid Approach

Abstract

A new type of hybrid prediction system (HPS) of the land surface air temperature (SAT) is described and its skill evaluated for one particular application. This approach utilizes sea-surface temperatures (SST) forecast by a dynamical prediction system, SINTEX-F2, to provide predictors of the SAT to a statistical modeling system consisting of a set of nine different machine learning algorithms. The statistical component is aimed to restore teleconnections between SST and SAT, particularly in the mid-latitudes, which are generally not captured well in the dynamical prediction system. The HPS is used to predict the SAT in the central region of Japan around Tokyo (Kantō) as a case study. Results show that at 2-month lead the hybrid model outperforms both persistence and the SINTEX-F2 prediction of SAT. This is also true when prediction skill is assessed for each calendar month separately. Despite the model's strong performance, there are also some limitations. The limited sample size makes it more difficult to calibrate the statistical model and to reliably evaluate its skill.