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

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. Deep learning models architecture used in this paper. (a) U‐net; (b) UNet++; (c) Squeeze‐and‐Excitation (SE) block.