Abstract
Lightning forecasting is a vital item in server convective system short-time forecasting. However, lightning parameterization in mesoscale numerical prediction models is still in its early stages of development. Several lightning parameterization schemes are implemented in the Weather Research and Forecasting (WRF) model. Data assimilation can provide a more accurate initial field, which could be useful for subsequent lightning forecasting. To evaluate its effect on lightning forecasting, a severe convective case that influenced Jiangsu and Anhui Province on 5 June 2009 is utilized and a series of experiments are conducted including assimilating radar reflectivity and lightning location network data via the three-dimensional variational (3DVar) method. Results show that data assimilation can effectively improve reflectivity forecasting and subsequent lightning forecasting. Lightning forecasting based on the PR92 lightning parameterization scheme, which is based on the convective cloud top height, offers a weaker magnitude forecast. The diagnostic method based on reflectivity and temperature has some spatial displacement. The potential forecast provided by lightning threat indexes produced an improvement in Anhui Province, while in other regions, it is located further east than the observation.
Highlights
During severe convective processes, lightning network observation systems can provide effective data which reflect the development of convective systems
In numerical weather prediction (NWP) models, the realization of lightning forecasts depends on the proper frame of the lightning parameterization scheme
Weather Research and Forecasting (WRF), as one of the mesoscale numerical prediction models, has started to offer intra-cloud and cloud-to-ground lightning forecasting based on some certain lightning parameterization schemes since version 3.5.1
Summary
During severe convective processes, lightning network observation systems can provide effective data which reflect the development of convective systems. In the past decades, based on the microphysical mechanisms and dynamic variables in convective clouds, many researchers have tried to find a reliable relationship between lightning data and other meteorological variables to obtain a proper observation operator for lightning data assimilation These variables are convective precipitation rate [7,8,9,10], convective available potential energy (CAPE) [11,12], maximum vertical velocity [13], proxy radar reflectivity [14,15,16,17], graupel mass [18], ice mass flux product [19,20], and updraft volume [21,22].
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