Abstract

Because explicit prediction of the electrical activity in storms is computationally expensive and the processes are still poorly understood, an attractive way to predict lightning flash rates in numerical models is to rely on correlations between the flash rate and available model parameters. Pre... Show moreBecause explicit prediction of the electrical activity in storms is computationally expensive and the processes are still poorly understood, an attractive way to predict lightning flash rates in numerical models is to rely on correlations between the flash rate and available model parameters. Predicted flash rates can be used for applications such as the parameterization to infer lightning-produced nitrogen oxides. In this study, the potential for six model parameters (precipitation ice mass, ice water path, ice mass flux product, updraft volume, maximum vertical velocity, and cloud top height) to predict lightning rate has been investigated in a cloud-resolving model framework. The Weather Research and Forecasting model (WRF) is used to simulate two different storms: the 10 July 1996 severe storm that occurred over the High Plains and the 13 July 2005 airmass thunderstorm near Huntsville, Alabama. It is shown that the WRF model reproduces the structure of the two storms. Results show that the maximum updraft velocity gives a good flash rate proxy for the severe storm. The ice mass flux product and precipitation ice mass can reproduce the flash rate trend but not the magnitude. The flash rate estimated from the cloud top height does not match the observed flash rate trend and value of the severe storm, but is in good agreement for the airmass thunderstorm. The ice water path predicts flash rate fairly well for the severe storm, but overpredicts it for the airmass thunderstorm. The updraft volume predicts flash rate poorly for both storms. Show less