Authors:Mengxuan Chen, Yunpu Xu, Qiuyan Sun, Han Zhang, Jiayi Lai, Zheng Zhou, Juepeng Zheng, Hongsong Meng, Nan Wei, Jinxiao Zhang, Xiongchuan Tan, Haodong Bian, Yinan Cai, Ge Yang, Fang Wang, Yunyun Liu, Conghui He, Runmin Dong, Lanning Wang, Yutong Lu, Yongjiu Dai, Haohuan Fu
Abstract:Seasonal forecasting of summer rainfall in East Asia remains a grand challenge, as predictability at 3 to 6 month lead times is constrained by the spring predictability barrier, weak large-scale signals, and localized nonlinear convective extremes. We address this challenge with CAPES, which integrates a kilometer-resolution coupled regional model with atmosphere, land, and ocean components and a data-driven AI seasonal forecasting system. At 15 km resolution, the fused workflow combines 174 numerical members from varying start times, physics schemes, and parameter perturbations with 1,600 AI members generated from initial and physical perturbations. Using the full LineShine system, CAPES completes ten annual 1,774-member hindcasts for 2016 to 2025 within 14.6 hours, improving the mean prediction score from ECMWF's 71.8 to 75.9 and delivering a major gain in operational forecasting capability. The 1-km configuration further enables fine-scale typhoon simulation and establishes the feasibility of kilometer-scale fused ensemble forecasting on a one-week timescale.
From: Mengxuan Chen [view email]
[v1]
Sun, 24 May 2026 06:52:51 UTC (8,035 KB)
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