Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming

This study presents a new method for assimilating lightning data into numerical models that is suitable for cloud-resolving scales (e.g., 3 km). The study utilized data from the Earth Networks Total Lightning Network at 9 km grid spacing to mimic the resolution of the Geostationary Lightning Mapper (GLM) that will be on the upcoming GOES-R satellites. The assimilation procedure was developed using the Weather Research and Forecasting (WRF) numerical model. The method (denoted MU) warms the most unstable low levels of the atmosphere at locations where lightning was observed but deep convection was not simulated based on the absence of graupel. Simulation results utilizing the new method are compared with a control simulation and a simulation employing the lightning assimilation method (FO) developed by Alexandre Fierro and colleagues. Unlike MU, the FO method increases relative humidity according to a nudging function dependent on the intensity of observed lightning and simulated graupel mixing ratio. Simulations are performed across the Central and Eastern United States for three separate severe storm cases during 2011. These cases exhibit a wide range of weather patterns and thunderstorm organization. When comparing simulation results with hourly NCEP stage IV radar and gauge precipitation observations, both the MU and FO assimilation methods produce an improved simulated precipitation field during the assimilation period and a short time afterwards based on subjective comparison and objective statistical scores. The assimilation methods commonly improve equitable threat scores by more than 0.1 and 50% during the assimilation period. Differing degrees of improvement from the assimilation methods depend on the weather pattern, with the MU method generally performing better in the simulation of isolated thunderstorms and other weakly forced deep convection. Biases in the precipitation, moisture, and temperature fields of the simulations also are examined and sometimes differ considerably between assimilation schemes. Based on performance and bias, the newly developed MU method is shown to be a viable alternative to the FO method, exhibiting utility in producing and locating thunderstorms where observed and providing a better analysis at low computational cost.