Third Conference on Meteorological Applications of Lightning Data

6.3

Use of high-resolution WRF simulations to forecast lightning threat

Eugene W. McCaul Jr., USRA, Huntsville, AL; and K. M. LaCasse, S. J. Goodman, and D. J. Cecil

Recent observational studies have confirmed the existence of a robust

statistical relationship between lightning flash rates and the amount of large

precipitating ice hydrometeors aloft in storms. This relationship is exploited,

in conjunction with the capabilities of cloud-resolving forecast models such as

WRF, to forecast explicitly the threat of lightning from convective storms using

selected output fields from the model forecasts. The simulated vertical flux of

graupel at -15C and the shape of the simulated reflectivity profile are tested

in this study as proxies for charge separation processes and their associated

lightning risk. Our lightning forecast method differs from others in that it is

entirely based on high-resolution simulation output, without reliance on any

climatological data.

Short (6-8 h) simulations are conducted for a number of case studies for which

three-dimensional lightning validation data from the North Alabama Lightning

Mapping Array are available. Experiments indicate that initialization of the

WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial

velocity fields, and METAR and ACARS data yield satisfactory simulations.

Analyses of the lightning threat fields suggests that both the graupel flux and

reflectivity profile approaches, when properly calibrated, can yield reasonable

lightning threat forecasts, although an ensemble approach is probably desirable

in order to reduce the tendency for misplacement of modeled storms to hurt the

accuracy of the forecasts. Our lightning threat forecasts are also compared

to other more traditional means of forecasting thunderstorms, such as those based

on inspection of the convective available potential energy field.

wrf recording  Recorded presentation

Session 6, Lightning Prediction Using Observations and Models
Wednesday, 23 January 2008, 10:30 AM-12:00 PM, 222

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