Forecasting lightning probabilities derived from the Lightning Potential Index using neural networks
- Deutscher Wetterdienst, Offenbach, Germany
Forecasting the occurrence of thunderstorms is a well-known challenge in weather prediction. Since a thunderstorm is by definition accompanied by at least one lightning, we aim to forecast the occurrence of at least one lightning within a pre-defined area and a pre-defined time-interval. Numerous prior research studies on forecasting and detecting lightnings provide a rich database of model diagnostics and observational data. One example of such a model diagnostic is the so-called (subgrid-scale) Lightning Potential Index (LPI), that was recently implemented in the operational ICON-model (1) and is now available operationally, even in the ICON-EU ensemble. On the other hand, there are extensive observation networks that provide lightning observations, such as the Linet-network (2) that provides lightning observations over Europe.
In our work, we use lightning observations from the Linet-network as ground truth and establish a translation from LPI to lightning probabilities. For this task, we trained neural networks to predict the desired lightning probabilities in a "global postprocessing mode", i.e. using the same network for the forecasts on the whole domain of ICON-EU, which is significantly larger than the domain of the Linet-network. We present the setup of the postprocessing method together with details of the training of the neural networks and show first results from its forecasts and their evaluation. In particular, these lightning probabilities outperform raw model probabilities as derived by counting the exceedance of a LPI-threshold and thereby avoid the need to even define such a threshold. We will also address aspects of producing stable operational forecasts by applying an ensemble of neural networks.
References:
(1) Schröder et al., 2022: Subgrid scale Lightning Potential Index for ICON with parameterized convection. Reports on ICON (10), DOI: 10.5676/DWD_pub/nwv/icon_010
(2) Betz et al., 2009: LINET - an international lightning detection network in europe. Atmos. Res., 91, 564–573, DOI: 10.1016/j.atmosres.2008.06.012
How to cite: Baumgartner, M., Schröder, G., and Primo, C.: Forecasting lightning probabilities derived from the Lightning Potential Index using neural networks, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-34, https://doi.org/10.5194/ems2023-34, 2023.
