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首页> 外文期刊>Atmospheric research >Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5
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Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5

机译：机器学习在大雹预测中的应用-从ERA5得出的雷达反射率，雷电发生和对流参数的重要性

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This study presents a concept for coupling remote sensing data and environmental variables with machine learning techniques for the prediction of large hail events. In particular, we want to address the following question: How would one improve the performance of large hail warnings / forecasts if thermodynamic and kinematic parameters derived from a numerical weather prediction model are combined with real-time remote sensing data? For this purpose, POLRAD radar reflectivity, EUCLID lightning detection data, and convective indices calculated from the ERAS reanalysis are combined and then compared with large hail reports from Poland (2008-2017). The data fusion of multiple sources, coupled with the machine learning approach, makes it possible to greatly improve the robustness of large hail prediction compared to any single product commonly used in operational forecasting. This is especially noticeable with the reduced number of false alarms. Although the created machine learning models are mainly driven by radar reflectivity, composite thermodynamic and kinematic indices such as Hail Size Index (HSI), Significant Hail Parameter (SHIP), Large Hail Parameter (LGHAIL), and WMAXSHEAR provide an added value to a model's performance. The accuracy achieved by a random forest model brings with it encouraging prospects for future research with respect to operational forecasters (who may fill in the gaps within NWP-derived data with remotely sensed measurement) and climatological studies that aim to investigate past and future changes in severe weather occurrences.
机译：这项研究提出了一种将遥感数据和环境变量与机器学习技术相结合以预测大雹事件的概念。特别是，我们要解决以下问题：如果将从数值天气预报模型得出的热力学和运动学参数与实时遥感数据结合起来，人们将如何改善大型冰雹警告/预报的性能？为此，将POLRAD雷达反射率，EUCLID雷电检测数据和通过ERAS重新分析计算出的对流指数进行了组合，然后与波兰的大量冰雹报告（2008-2017年）进行了比较。与操作预测中通常使用的任何单个产品相比，多源的数据融合以及机器学习方法使大大提高冰雹预测的鲁棒性成为可能。随着误报数量的减少，这一点尤其明显。尽管创建的机器学习模型主要由雷达反射率驱动，但是复合的热力学和运动学指标（例如冰雹尺寸指数（HSI），有效冰雹参数（SHIP），大冰雹参数（LGHAIL）和WMAXSHEAR）为模型的附加价值性能。随机森林模型实现的准确性带来了令人鼓舞的前景，即有关业务预报员（他们可能用遥感测量方法填补NWP数据中的空白）和旨在研究气候变化过去和将来变化的气候研究方面的前景。恶劣的天气情况。

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