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.

Quantitative precipitation nowcasting (QPN) is a highly challenging task in weather forecasting. The ability to provide precise, immediate, and detailed QPN products is necessary for a variety of situations, including storm warnings, air travel, and large gatherings. To address this challenge, this paper proposes a new transformer LPT(Lightweight Physics-informed Transformer)-QPN for QPN tasks, utilizing vertical cumulative liquid water content (VIL) products. This model adopts novel transformer modules to model long-term evolution of precipitation and incorporates multi-head squared attention (MHSA) to model its highly non-linear relationships while reducing computational complexity. The results of experimental evaluations demonstrate the superiority of LPT-QPN when compared to existing state-of-the-art QPN models. In particular, the LPT-QPN model demonstrates greater accuracy for long lead time and in high-intensity areas, confirmed in both quantitative and qualitative evaluations. Additionally, through three customized fine-tuning schemes, we are able to further improve the predictability of the LPT-QPN model for specific precipitation events. By incorporating physical constraints of convection diffusion equation, our approach offers novel perspectives for future explorations that combine physical prior knowledge and deep learning techniques.

In this paper, we describe an implementation of the fast spherical harmonic transform (SHT) algorithm in the Yin–He global spectral model (YHGSM). A new analysis method is proposed to study the potential instability of interpolative decomposition and to evaluate the performance of fast SHT on the MilkyWay-2 supercomputer. The novel aspect of the proposed method is the use of the computational complexity analysis method by studying the properties of interpolative decompositions. Furthermore, three test cases are conducted to verify fast SHT in YHGSM. The results demonstrate that fast SHT is feasible and efficient for reducing the computational complexity and memory cost of SHT while still keeping enough accuracy.