Experimental Study of Cloud-to-Ground Lightning Nowcasting with Multisource Data Based on a Video Prediction Method

Abstract

The evolution of lightning generation and extinction is a nonlinear and complex process, and the nowcasting results based on extrapolation and numerical models largely differ from the real situation. In this study, a multiple-input and multiple-output lightning nowcasting model, namely Convolutional Long- and Short-Term Memory Lightning Forecast Net (CLSTM-LFN), is constructed to improve the lightning nowcasting results from 0 to 3 h based on video prediction methods in deep learning. The input variables to CLSTM-LFN include historical lightning occurrence frequency and physical variables significantly related to lightning occurrence from numerical model products, which are merged with each other to provide effective information for lightning nowcasting in time and space. The results of batch forecasting tests show that CLSTM-LFN can achieve effective forecasts of 0 to 3 h lightning occurrence areas, and the nowcasting results are better than those of the traditional lightning parameterization scheme and only inputting a single data source. After analyzing the importance of input variables, the results show that the role of numerical model products increases significantly with increasing forecast time, and the relative importance of convective available potential energy is significantly larger than that of other physical variables.

Performance of Lightning Potential Index, Lightning Threat Index, and the Product of CAPE and Precipitation in the WRF Model

“…The first 12 hr of each simulation are considered the spin‐up time, which is consistent with recent similar studies conducted by Guo et al. (2022) and Federico et al. (2022).…”

“…The first 12 hr of each simulation are considered the spin-up time, which is consistent with recent similar studies conducted by Guo et al (2022) and Federico et al (2022). Table 2 summarizes the model configurations and physical parameterizations, the selection of which is based on previous studies (e.g., Gharaylou et al, 2019;Yair et al, 2010).…”

The impacts of shipping emissions on lightning: roles of aerosol-radiation-interactions and aerosol-cloud-interactions

“…We thus choose three periods with relatively high lightning flash counts scattered in January 2013 (4-6 January), 2015 (25-29 January), and 2018 (28-31 January) as the cases for WRF-Chem simulation (figure 1(d)). We follow previous studies (Federico et al 2014, Gharaylou et al 2020, Guo et al 2022 to set the first 12 h in each simulation for spin-up to allow the meteorological conditions to be adjusted to different scenarios.…”

Correlation analysis between lightning flashes and rainfall rate during a flash flood thunderstorm

“…It can be very destructive as a result of intense rainfall that occurs over short periods and often results in flash floods. Many studies showed that the cloud-to-ground lightning flash activities of a thunderstorm could be used in local short term forecasting of heavy rain or precipitation estimation and flash flood or hailstone [11]- [14]. Thus, high precipitation corresponds to strong atmospheric activity in convective systems.…”