Abstract
Two new approaches are proposed and developed for making time- and space-dependent, quantitative short-term forecasts of lightning threats, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the Weather Research and Forecasting (WRF) model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models.
One method is based on upward fluxes of precipitating ice hydrometeors in the mixed-phase region at the −15°C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash-rate proxy fields against domain-wide peak total lightning flash-rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. The blended solution proposed in this work is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second.
Simulations of selected diverse North Alabama cases show that the WRF can distinguish the general character of most convective events, and that the methods employed herein show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically based model versions, physical parameterizations, initialization techniques, and ensembles of forecasts become available.
Forecasting Lightning Threat Using Cloud-Resolving Model Simulations
