A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms

Abstract

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 minute forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

End-to-End Prediction of Lightning Events from Geostationary Satellite Images

“…Nevertheless, it is not a direct comparison and hence only provides an indicator for possible improvements using the method presented in this manuscript. The authors of [14] state a CSI for the persistence algorithm of 26%, which is consistent with the values reported in our work; see Table A3. For the physical nowcasting method, they state a CSI of 38%; thus, the improvement factor is of about 1.4 and hence significantly below the values achieved by our learned classifier.…”

“…We evaluate our method on the testing data set aside from the original 2016/17 data set (Table A1 in the Appendix A.1), as well as testing data from the month of August and September of 2021, for which comparison data with a nowcasting method [14] at DWD were available. In line with good experimental practice, all hyperparameter tuning has been concluded using validation data before running any inference on any test data has been performed (with frozen, final hyperparameter settings).…”

“…The DWD currently works on an improvement of the nowcasting applied within the 24/7 nowcasting method, discussed in detail in [14]. In order to obtain a first hint of the possible improvements of a machine-learning-based method, the factor of improvement between persistence and nowcasting was calculated for the 120 min prediction for both approaches.…”

“…For example, [13] use the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16, as well as satellite and solar zenith angles and geo-coordinates to predict "intense convection". In [14], satellite-based information is combined with lightning data from the Vaisala Global Lightning Detection Network (GLD360) and information from NWP. Moreover, a combination of satellite and radar information is applied; for example, [15] make use of five temporal images from two ABI channels to predict convective regions, derived from Multi-Radar Multi-Sensor (MRMS) precipitation types.…”

“…However, it remains unclear whether physical nowcasting methods are capable of making the best possible use of the available information given in the satellite image and lightning data. As discussed before, current approaches, e.g., [14,16,17], do not take into account the decay or development of (new) cells. Corresponding information may be hidden in the input data and unused.…”