<p>By using a multi-source data set consisting of high resolution satellite, radar, lightning, and model data this study presents the analysis of characteristics of deep convective systems over Germany and first results of a new model to predict the remaining lifetime of existing thunderstorms. Contrary to previous studies, the analysis was performed for the full mixture of observed convective systems regardless of their organization type, since our focus is an operational forecasting environment where no simple method is available to differentiate organization types. Basis for the study are all deep convective cell detections in satellite data (using Cb-TRAM, Thunderstorm Tracking and Monitoring) in a five month period (June 2016, May, June, and July 2017, and June 2018). The lifetimes of all cells are normalized, averaged and separated into life cycle phases to investigate the behavior of the parameters from the different data sources during the detected lifetime. Furthermore, the thunderstorm cells are sorted by their lifetime to determine differences between the characteristics of long- and short-lived convective systems. Parameters with predictive skill are then combined with fuzzy logic to determine the actual stage of a thunderstorm, and to nowcast its remaining lifetime. It will be shown that the new lifetime prediction model contributes to an improvement of the thunderstorm nowcasting.</p>
<p>Thunderstorms constitute a major hazard to society and economy. Especially in light of the expected increase of extreme weather events due to climate change, reliable thunderstorm warnings become ever more important. However, as lightning is not directly computed in numerical weather prediction (NWP) simulations, the appearance of thunderstorms in forecast output remains elusive. In this work, we introduce SALAMA, a tool to identify signatures of lightning activity in NWP simulations using a feedforward artificial neural network (ANN). It infers in a reliably calibrated manner the probability of lightning occurrence at some point in space and time, given only a set of local input parameters that are extracted from NWP simulations and related to thunderstorm development. We train the neural network with ensemble forecasts from ICON-D2-EPS during the summer period of 2021. The skill of SALAMA is measured through established scores from meteorology and machine learning. We study in detail how the forecast skill depends on the lead time of the forecast as well as the spatial scale of the forecast objects and put particular emphasis on a careful estimation of model uncertainty. Even with a relatively simple ANN architecture and local input parameters, we find a forecast skill superior to traditional approaches in the literature. SALAMA is ready for operational use.</p>
Thunderstorm forecasts with lead times of more than one hour usually rely on the post-processing of numerical weather prediction (NWP) data. Thanks to machine learning methods, this post-processing step has seen encouraging improvement in recent years. In this work, we introduce SALAMA, a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction data. The model is trained on convection-resolving ensemble forecasts over Central Europe while lightning observations serve as ground truth. We believe that our work represents the first application of a neural network for thunderstorm forecasting to ensemble data with a fine resolution of only 2km. We solve a binary classification task: 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. Particular emphasis is put on making the model reliable. We quantify classification skill through established scores from the meteorological and machine learning community and carefully estimate model uncertainty. For lead times up to eleven hours, we find a classification skill superior to classification based only on convective available potential energy. Varying the spatiotemporal criteria by which we associate lightning observations with NWP data, we estimate the advection speed of thunderstorms in the atmosphere and show that the time scale for skillful thunderstorm predictions increases linearly with the spatial scale of the forecast. All predictors entering our model are available in real time, which makes SALAMA readily available for operational use.
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