The Investigation of the Performance of Reanalyzed Global Forecast System (GFS( Data for Identifying evening thunderstorms in Iran

Thunderstorms, also known as electrical storms or lightning storms, are one of the most damaging weather hazards and often damaging to the environment, crops, cities, and property. Therefore, forecasting them is very important for decreasing the possible damages. To forecast these phenomena, investigating the thermodynamic structure of the atmosphere and analyzing instability indices is necessary. The purpose of this study is to investigate the general characteristics of thunderstorms and the ability of GFS reanalysis data in identifying the spatial distribution of thunderstorms in Iran. For this aim in addition to GFS data, 8-year thunderstorm reports (2008 to 2015) in 361 weather stations in Iran from April to May were also used. At first, based on the thunderstorm frequency, each data series was divided into 4 quarters. Then, 10 atmospheric instability indices were calculated for daily GFS data and the results were analyzed. The results showed that the CIN index has the best performance. However, this index showed more error in Quarter-4 which had strong instability. The KI index showed moderate performance in forecasting weak instabilities but showed better results as instability increased in the fourth quarter. Both TT and 4LFTX indices had the same results and showed strong instabilities in the western parts of the country. Investigation of the average ability of the indices, induced that the four indices, including CIN, TT, 4LFTX, and KI had good performance in detecting instability. This performance varies from 90% in the CIN index to 60% in the KI index. The other investigated indices did not have an acceptable performance. It is recommended to redefine the threshold values of the instability indices according to the climate type in the studied stations.