Evaluation of Lightning Prediction by an Electrification and Discharge Model in Long-Term Forecasting Experiments

Abstract

Over nearly three rainy seasons of lightning activity in North China, numerical prediction experiments were carried out using the Weather Research and Forecasting model coupled with electrification and discharge schemes (WRF-Electric). The numerical forecast results were evaluated using the neighborhood-based equitable threat score (ETS) and fraction skill score (FSS) verification methods based on nationwide observational lightning data. An algorithm was used to generate the coverage of the total flash (intracloud and cloud-to-ground flashes) by fitting the cloud-to-ground flash data. The numerical results showed that the region of lightning activity could be well predicted by the mesoscale WRF-Electric model, particularly during a 6–12-hour forecasting period. The average ETS score of the 6–12-hour forecasting period was 0.34 for a 20 km neighborhood radius. The predictive skill of the model varied not only monthly but also diurnally. The model showed better forecasting skills during the main rainy season (June–July–August) and at 14 : 00–20 : 00 local time. The predictability of the model was enhanced with increasing thunderstorm scale. On the other hand, the coverage of predicted lightning activity was relatively concentrated, and the lightning flash density was higher than the observations. The main discrepancies in the model prediction were related to the design of the discharge parameterization. Thus, in discharge parameterization, the initial threshold for lightning should be modified according to the model resolution, while the magnitude of the neutralization charge in a single discharge should be referenced to the observational results.

ECMWF Lightning Forecast in Mainland Portugal during Four Fire Seasons

“…These values decrease to 0.269 and 0.258, for the same scores, when assessing the 0.5 • resolution data. The ETS values are consistent with those calculated by Dafis et al [44] and those of Xu et al [45], both cases referring to the prediction verification with an WRF-E.…”

“…This procedure is handled as a dichotomous approach, where the data are treated as either yes or no based on whether there is a match between the observed and predicted data or not. In the case of lightning forecast, the use of contingency table has also been used in several studies [43][44][45][46][47]. To assess the model's performance to forecast the occurrence of a given lightning strike, a contingency table was computed (Table 1) to calculate some skill scores.…”

“…This procedure is handled as a dichotomous approach, where the data are treated as either yes or no based on whether there is a match between the observed and predicted data or not. In the case of lightning forecast, the use of contingency table has also been used in several studies [43][44][45][46][47].…”