可解释深度学习用于龙卷风暴中雷达反射率的概率临近预报

中文摘要 AI 辅助

龙卷风对美国人的生命和财产构成重大风险，平均每年造成超过50人死亡和1亿美元财产损失。当龙卷风可能发生时，天气雷达通过提供风暴形态、风暴运动和强度趋势的信息，为预报员提供关键信息。卫星和数值天气预报模型运行等附加工具可以提供有用的短期信息，以了解风暴特征的变化。这项工作展示了一个U-Net深度学习系统，用于临近预报龙卷风生成后雷达反射率的演变，该系统通过综合大量输入数据（如雷达图像、近风暴环境数据）并生成雷达反射率预测，可为预报员提供价值。模型的输入是多雷达多传感器（MRMS）数据集的雷达图像和高分辨率快速刷新（HRRR）数值天气预报模型的近风暴环境数据。U-Net在龙卷风暴数据集上训练，以在龙卷风生成后产生30分钟的雷达反射率概率预测，概率预测通过预测SinhArcSinh（SHASH）分布的参数获得。该模型产生物理上真实的雷达演变预测，达到与HRRR下一小时预报相当的技能，显示出合理的概率校准，并伴随多种可解释性方法以提高最终用户的理解。此外，该模型的预测可以比数值天气预报模型的预测更快获得。随着进一步发展，该模型可以扩展以在业务环境中临近预报雷达反射率演变。

英文摘要

Tornadoes pose substantial risk to human life and property in the United States, causing more than 50 fatalities and \$100 million of property damage on average annually. When tornadoes are likely, weather radar provides critical information for forecasters by providing information on storm morphology, storm motion, and intensity trends. Additional tools such as satellite and numerical weather prediction model runs can provide useful short-term information for understanding changes in storm characteristics. This work demonstrates a U-Net deep-learning system for nowcasting the evolution of radar reflectivity following tornadogenesis, which can provide value to forecasters by synthesizing large amounts of input data (e.g., radar imagery, near-storm environment data) and generating predictions of radar reflectivity from its inputs. Inputs to the model are radar imagery from the Multi-Radar Multi-Sensor (MRMS) dataset and near-storm environment data from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model. The U-Net is trained on a dataset of tornadic storms to produce 30 minutes of probabilistic predictions of radar reflectivity following tornadogenesis, with probabilistic predictions obtained by predicting parameters of the SinhArcSinh, or SHASH, distribution. The model produces physically realistic predictions of radar evolution, achieves comparable skill to next-hour forecasts from the HRRR, demonstrates reasonable probabilistic calibration and is accompanied by a variety of explainability methods to improve understanding by end users. Additionally, predictions from the model can be obtained much more quickly than those from a numerical weather prediction model. With further development, this model could be extended to nowcast radar reflectivity evolution in an operational setting.

发表机构

• University of Oklahoma, School of Meteorology（俄克拉荷马大学气象学院）
• National Science Foundation Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（国家科学基金会天气、气候和沿海海洋学可信人工智能研究所）
• University of Oklahoma, School of Computer Science（俄克拉荷马大学计算机科学学院）

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