Deep learning-based precipitation bias correction approach for Yin–He global spectral model

Abstract

In this paper, a data-driven bias correction approach based on deep learning is proposed, which is appropriate for the Yin–He global spectral model (YHGSM) re-forecasting. The proposed architecture involves four U-Net-based networks estimating the proper bias correction models for YHGSM re-forecasting that consider as correction factors the geopotential, specific humidity, and vertical velocity on three pressure levels from the YHGSM model. The proposed models are then evaluated for their bias correction capability on the 3-h cumulative precipitation over the region of China between 15°–54.5° N, and 63°–122.5° E. The results revealed that U-Net-based models could reduce the root mean squared error (RMSE) and improve the threat scores (TSs), especially for heavy precipitation and rainstorms.