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Atmosphere 2018, 9(3), 99; https://doi.org/10.3390/atmos9030099

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

1
School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
3
State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Received: 24 November 2017 / Revised: 7 February 2018 / Accepted: 7 March 2018 / Published: 10 March 2018
(This article belongs to the Section Climatology and Meteorology)
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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. View Full-Text
Keywords: lightning parameterization; data assimilation; lightning rate forecast lightning parameterization; data assimilation; lightning rate forecast
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Wang, Y.; Yang, Y.; Jin, S. Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods. Atmosphere 2018, 9, 99.

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