Abstract
As the first Geostationary Satellite with the LMI (Lightning Mapping Imager) instrument aboard running over the eastern hemisphere, FY-4A (Feng-Yun-4A) can better indicate severe convection and compensate for the limitations of radar observation in temporal and spatial resolution. In order to realize the application of FY-4A lightning data in numerical weather prediction (NWP) models, a logarithmic relationship between FY-4A lightning density and maximum radar reflectivity is presented to convert FY-4A lightning data into maximum FY-4A proxy reflectivity. Then, according to the profiles of radar reflectivity, the maximum FY-4A proxy reflectivity is extended to 3D FY-4A proxy reflectivity. Finally, the 3D FY-4A proxy reflectivity is assimilated in RMAPS-ST (Rapid-refresh Multi-scale Analysis and Prediction System—Short Term) to compare with radar assimilation. Four groups of continuous cycling data assimilation and forecasting experiments are carried out for a severe rainfall case. The results demonstrate that cycling assimilation of 3D FY-4A proxy reflectivity can adjust the moisture condition effectively, and indirectly affects the temperature and wind fields, then makes the thermal and dynamic analysis more reasonable. The Fractions Skill Scores (FSSs) show that the rainfall forecasts are improved significantly within 6 h by assimilating 3D FY-4A proxy reflectivity, which is similar to the parallel experiments in assimilating radar reflectivity. In addition, other cycling data assimilation experiments are carried out in mountainous areas without radar data. The improvement of precipitation forecasts in mountainous areas further proves that the application of assimilating 3D FY-4A proxy reflectivity can be considered a useful substitute where observed radar data are missing. Through the two severe rainfall cases, this method could be framed as an example of how to use lightning for data assimilation.
Highlights
Lightning is a common discharge phenomenon in convective weather that exists in clouds or cloud to ground [1,2]
In order to realize the application of FY-4A lightning data in numerical weather prediction (NWP) models, we tried to convert FY-4A lightning data into 3D FY-4A proxy reflectivity by a set of quantitative relationship
In order to realize the application of FY-4A lightning data in NWP models, a set of relationships is present which convert the FY-4A lightning density into 3D FY-4A proxy reflectivity
Summary
Lightning is a common discharge phenomenon in convective weather that exists in clouds or cloud to ground [1,2]. With the accumulation of high-quality lightning data, the application of lightning assimilation techniques in NWP models, aiming to improve the forecasting skills of convective precipitation, has been a major scientific topic recently [12,13,14,15]. It has been proved that the assimilation of lightning data can improve the initialization of NWP, the difficulty still exists that lightning is not a modeled or prognostic variable in most existing NWP models. The aim of assimilating lightning data is to find a suitable observation operator to transfer model or prognostic variables to lightning data, or to convert lightning data into some other observations which can be assimilated in the existing data assimilation (DA) systems [16,17]
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