Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

Abstract

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.

An Evaluation of Relationships between Radar-Inferred Kinematic and Microphysical Parameters and Lightning Flash Rates in Alabama Storms

“…Although single-parameter relations are the focus of this study, other studies have proposed two-(or multi-) parameter relations based on combined kinematic and microphysical conditions (e.g., ice mass flux [13,50]) or radar and environmental conditions [48]. Based on the results herein and other studies [13,48,50,51,91,92], more research on multi-parameter flash rate parameterization equations is likely warranted, especially if tests are conducted on a much larger sample size in a wider variety of conditions to validate generality. Another possibility to explore in future work is the use of multiple linear regression using combinations of some or all of the kinematic and microphysical parameters explored in this study.…”

“…In contrast to studies using explicit cloud electrification and lightning flash rate [46,47], the flash rate used for LNO X production in these chemical transport models is estimated from flash rate parameterization schemes, which utilize model-predicted storm kinematic and microphysical quantities and are often based on radar-observed relationships [8][9][10]13,14,25,48,49]. Similarly, the initiation, cessation, and frequency of lightning have been forecasted using flash rate parameterizations with model-predicted [50,51] or radar-observed [52][53][54][55] kinematic and microphysical parameters as inputs.…”

Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast

“…Finally, we verify the validity of each module of our model. 1 https://keras.io We choose parameters that are closely related to lightning [5], [7], [8], [15], [16]: the mixing ratio of ice, snow and graupel, the maximum vertical wind speed and precipitation. A detailed description of the selected parameters is shown in Table 2.…”

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

“…Pseudo-water vapor observations have been created and assimilated through the 3DVAR method, which has effectively improved the forecasting skill of severe convective precipitation [24][25][26]43]. Many notable results have been reported with the 3DVAR method [44][45][46][47][48][49]. The assimilation scheme based on water vapor is a simple and effective method.…”