Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Yuanyuan Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, Gao Mei

Geoscientific model development · 30 June 2023 · China Meteorological Administration (CMA)

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In one sentence. Physics-constrained deep learning (CGsNet) nowcasts convective wind gusts from radar at 0.01° and 6-minute resolution for 0-2 h, beating NWP-based nowcasting.

Abstract

Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CGs nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h of quantitative CGs nowcasting, first achieving minute-kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations, it can produce realistic and spatiotemporally consistent ASWS predictions in CGs events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01° × 0.01° and a 6-minute temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CGs nowcasting technology can be applied to provide real-time quantitative CGs forecasts and alerts the damaging wind events in meteorological services.

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