Lightning Data Assimilation Method for the Local Analysis and Prediction System (LAPS): Impact on Modeling Extreme Events

Radar data provide valuable information about storm development, track, and intensity. However, radar data are not available or are very limited over most parts of the Earth, including oceans, mountains, and regions with sparse or no radar networks. Accurate, timely lightning data provide supplemental information about convective activity and can be used as a proxy for radar reflectivity. Moreover, data from both regional and global lightning detection networks can be used for rainfall estimation and data assimilation into NWP models over data-sparse regions.

In this paper, the focus is on lightning data assimilation in support of numerical weather prediction. Specifically, a new lightning data assimilation (LDA) technique that has been developed for the Local Analysis and Prediction System (LAPS) is described. The technique uses lightning data from regional and global lightning detection networks such as Vaisala's National Lightning Detection Network (NLDN) and Global Lightning Dataset (GLD360), respectively. The method utilizes the relationship between the lightning rate and radar reflectivity. To investigate the lightning-reflectivity relationship, lightning and radar data over the continental United States and adjacent ocean areas were collected and analyzed. The results show a strong log-normal correlation between the lightning rates and reflectivity values. The LDA method modifies the initial hydrometeor fields and the resulting analysis is used to initialize the WRF model. The LDA method introduces significant differences in storm structure and dynamics compared to the cases without LDA. These differences are discussed together with model verification results.

Plans are underway to share the lightning data assimilation method with the modeling community as a part of the public LAPS repository.