Comparison of the WRF-FDDA-Based Radar Reflectivity and Lightning Data Assimilation for Short-Term Precipitation and Lightning Forecasts of Severe Convection

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (23), p.5980
Main Authors: Wang, Haoliang, Yuan, Shuangqi, Liu, Yubao, Li, Yang
Format: Article
Language:English
Subjects:
Convection
Data assimilation
Data collection
Heat
Lightning
lightning data assimilation
lightning forecast
Methods
Precipitation
precipitation forecast
Radar
Radar data
radar reflectivity data assimilation
Rain
Reflectance
severe convective weather
Temperature
Weather forecasting
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Summary:This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14235980