Lightning activity associated with precipitation and CAPE over Bangladesh

Abstract

Satellite‐derived lightning data for 17 years (1998–2014) were used to evaluate the relation between environmental factors and lightning activity over the Bangladesh landmass. Time series convective available potential energy (CAPE) data were extracted from ERA‐40 reanalysis data while total and convective rainfalls were obtained from Tropical Rainfall Measuring Mission's monthly products. In addition, the product of CAPE and precipitation was computed and used as an additional variable. Three timescales – monthly, seasonal and annual – were utilized to determine the influence of precipitation and CAPE on lightning activity. The results indicated that CAPE stands out as an important variable at all of these timescales for predicting the occurrence of lightning. The correlation coefficient (r) between CAPE and lightning activity was found to be 0.902 (monthly), 0.703 (pre‐monsoon), 0.550 (monsoon) and 0.702 (annual), respectively. Total rain showed strong positive correlation with lightning on monthly scale (r = 0.734) and in the pre‐monsoon season (r = 0.701). However, such relationship was moderate during monsoon (r = 0.455). In contrast, convective rain showed slightly higher correlation during monsoon (r = 0.587) compared with that of pre‐monsoon season (r = 0.532). Because of strong seasonality in the data, convective rain did not exhibit strong relationship on annual scale (r = 0.227). The product variable (e.g. CAPE × precipitation) showed significant correlation on monthly (r = 0.895) and seasonal scales (r = 0.818 during pre‐monsoon and 0.686 in monsoon) but its influence appears to diminish on a longer timescale (r = 0.375). Spatial maps of correlation coefficient revealed significant positive correlation along relatively drier northern parts of Bangladesh. As lightning‐related fatality is on the rise, this study, first of its kind, is expected to inform public policy and provide information necessary for effective management of this atmospheric phenomenon to save lives and property in Bangladesh.

Performance of a simple reanalysis proxy for U.S. cloud‐to‐ground lightning

“…Time averaging often reduces noise and enhances statistical relations, but, in this case, time averaging serves to highlight deficiencies of CP during the warm season. Dewan et al () also found low correlation of annual numbers of total flashes over Bangladesh with CP, but found a statistically significant relation with CAPE, which is not present here (not shown). The correlation of annual CONUS CP with counts of positive CG flashes is considerably larger ( r = 0.8; Figure ), although that correlation drops to 0.52 when the largest annual value (2016) is removed.…”

“…Furthermore, the use of spatially complete reanalysis data allows us to form the product of collocated CAPE and precipitation rate (denoted CP) on a spatially resolved grid and to examine regional features of the association of the CAPE‐precipitation product with CG lightning flash counts. A similar study of lightning over Bangladesh, where lightning is an especially dangerous hazard Dewan et al (), found that the CP was more strongly associated with total lightning flash counts during the pre‐monsoon season than during the monsoon season (Dewan et al, ).…”

Influence of aerosols on lightning activities in central eastern parts of China

“…CAPE represents the potential buoyancy of idealized rising air parcel and thus is a measure of the instability of atmosphere. The relationship between CAPE and lightning activity has been found in many other regions over the world such as India, Southeast Asia, and Bangladesh Pawar et al, 2012;Siingh et al, 2013;Murugavel et al, 2014;Dewan et al, 2018). Pawar et al (2012) studied the relationship between CAPE and lightning activity over central India during summer monsoons.…”

“…Lightning is also one of the important sources of nitrogen oxides in the atmosphere, and thus the occurrence of lightning has a significant influence on global atmospheric chemistry (Schumann and Huntrieser, 2007;Murray, 2016;Tost, 2017). Therefore, research on the response of lightning to various meteorological factors and climate change remains an important research topic (Reeve and Toumi, 1999;Xiong et al, 2006;Romps et al, 2014;Kotroni and Lagouvardos, 2016;Dewan et al, 2018;Finney et al, 2018). Therefore, research on the response of lightning to various meteorological factors and climate change remains an important research topic (Reeve and Toumi, 1999;Xiong et al, 2006;Romps et al, 2014;Kotroni and Lagouvardos, 2016;Dewan et al, 2018;Finney et al, 2018).…”

Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

“…The CAPE model uses a threshold of Convective Available Potential Energy (CAPE) to assess the risk of lightning. Assessing the level of CAPE which is well understood to be reflective of environments favorable to the development of lightning, 22,24,56 the CAPE model would be a more objective comparative scheme. Similar to the E-FIELD model, the corresponding threshold for each subset and lead time is defined in a way that the CSI is maximized.…”