用深度学习提升降水预测，尤其擅长重降水事件。
Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation
- 基于气象场数据，设计信息平衡机制应对降水分布长尾问题。
- 重降水预测精度优于现有AI模型，接近数值预报水平。
- 可嵌入任意全球气候模型，适合气象预报与灾害预警场景。
近期深度学习天气预报模型在气象变量预测上已超越传统数值模型，但在降水预报方面仍有较大提升空间，尤其是强降水事件。为此，我们提出全球深度学习模型Leadsee-Precip，从气象环流场生成降水。该模型采用信息平衡机制，应对降水数据长尾分布带来的预测挑战，并使用更精确的卫星和雷达反演降水数据作为训练目标。相比其他人工智能全球天气模型，Leadsee-Precip对强降水的模拟更贴近观测结果，性能可与全球数值天气预报模型竞争。该模型可集成至任一全球环流模型中生成降水预报。但预测环流场与真实环流场的偏差可能削弱降水预测效果，未来可通过基于预测环流场的进一步微调加以缓解。
原文摘要 · Abstract (English)
Recently, deep-learning weather forecasting models have surpassed traditional numerical models in terms of the accuracy of meteorological variables. However, there is considerable potential for improvements in precipitation forecasts, especially for heavy precipitation events. To address this deficiency, we propose Leadsee-Precip, a global deep learning model to generate precipitation from meteorological circulation fields. The model utilizes an information balance scheme to tackle the challenges of predicting heavy precipitation caused by the long-tail distribution of precipitation data. Additionally, more accurate satellite and radar-based precipitation retrievals are used as training targets. Compared to artificial intelligence global weather models, the heavy precipitation from Leadsee-Precip is more consistent with observations and shows competitive performance against global numerical weather prediction models. Leadsee-Precip can be integrated with any global circulation model to generate precipitation forecasts. But the deviations between the predicted and the ground-truth circulation fields may lead to a weakened precipitation forecast, which could potentially be mitigated by further fine-tuning based on the predicted circulation fields.
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