Thunderstorms pose a major hazard to society and economy, which calls for reliable thunderstorm forecasts. In this work, we introduce a Signature-based Approach of identifying Lightning Activity using MAchine learning (SALAMA), a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction (NWP) data. The model is trained on convection-resolving ensemble forecasts over Central Europe and lightning observations. Given only a set of pixel-wise input parameters that are extracted from NWP data and related to thunderstorm development, SALAMA infers the probability of thunderstorm occurrence in a reliably calibrated manner. For lead times up to eleven hours, we find a forecast skill superior to classification based only on NWP reflectivity. Varying the spatiotemporal criteria by which we associate lightning observations with NWP data, we show that the time scale for skillful thunderstorm predictions increases linearly with the spatial scale of the forecast.

翻译：雷暴对社会和经济构成重大威胁，因此需要可靠的雷暴预报。本研究提出了基于机器学习的闪电活动特征识别方法（SALAMA），这是一种用于识别数值天气预报（NWP）数据中雷暴发生的前馈神经网络模型。该模型基于中欧地区对流解析集合预报数据与闪电观测数据训练。通过仅提取NWP数据中与雷暴发展相关的逐像素输入参数，SALAMA能以可靠校准的方式推断雷暴发生的概率。在长达11小时的预报时效内，我们发现其预报技巧优于仅基于NWP反射率数据的分类方法。通过改变将闪电观测与NWP数据关联的时空准则，我们证明雷暴有效预报的时间尺度随预报空间尺度线性增长。