FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model

• URL: http://arxiv.org/abs/2310.19822v1
• Date: Wed, 25 Oct 2023 02:16:02 GMT
• Title: FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model
• Authors: Xiaohui Zhong and Lei Chen and Jun Liu and Chensen Lin and Yuan Qi and Hao Li
• Abstract summary: We develop the FuXi-Extreme model to restore finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. FuXi and FuXi-Extreme show superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES.
• Score: 14.19376315634697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, ML models face a common challenge: as forecast lead times increase, they tend to generate increasingly smooth predictions, leading to an underestimation of the intensity of extreme weather events. To address this challenge, we developed the FuXi-Extreme model, which employs a denoising diffusion probabilistic model (DDPM) to restore finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. An evaluation of extreme total precipitation ($\textrm{TP}$), 10-meter wind speed ($\textrm{WS10}$), and 2-meter temperature ($\textrm{T2M}$) illustrates the superior performance of FuXi-Extreme over both FuXi and HRES. Moreover, when evaluating tropical cyclone (TC) forecasts based on International Best Track Archive for Climate Stewardship (IBTrACS) dataset, both FuXi and FuXi-Extreme shows superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES.

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