A hybrid fusion precipitation bias correction approach for Yin‐He global spectral model

Abstract

In this article, we explored the performance of several fusion strategies for bias correction of precipitation on U-Net-based-models and proposed a simple but efficient hybrid fusion strategy. The geopotential, vertical velocity, specific humidity and 3-h cumulative precipitation from Yin-He global spectral model (YHGSM) re-forecast products are used as multiple correction factors, and the 3-h cumulative precipitation calculated from ERA5 are used as labels. Experimental results reveal that, our hybrid fusion strategy can reduce the number of parameters by about 40% and obtain better fraction skill score (FSS) and threat score (TS). For TS of 0.1, 3.0, 10.0 and 20. 0 mm, U-Net with hybrid fusion improves 31.8%, 97.0%, 314.0% and 576.0% than YHGSM, improves 1.3%, 3.0%, 3.6% and 10.5% than the classical U-Net. This hybrid fusion strategy should provide a feasible approach to utilize multiple information inputs more efficiently in geophysics field.