Abstract
In this paper, we propose an H-infinity (H∞) filtering approach for the prediction of bias in post-processing of model outputs and past measurements. This method adopts minimax strategy that is a solution for zero-sum games. The proposed H∞ filtering approach minimizes maximum possible errors whereas a recently common approach that adopts the Kalman filtering (KF) minimizes the mean square errors. The proposed approach does not need the information of noise statistics unlike the method based on the KF, while training process is required. We show that the proposed approach outperforms the method based on the KF in experiments by applying real weather data in Korea.
