Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

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.

Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

“…(2007) utilized the Kain‐Fritsch (KF) scheme to produce convection where lightning was observed, and to suppress spurious convection where no lightning was observed. In other studies, lightning was used as a proxy of radar reflectivity and was then assimilated by different methods (Benjamin, 2006; Benjamin et al., 2007; Chen, Yu, et al., 2020; Wang et al., 2014; Yang et al., 2015).…”

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

“…The empirical relationships between vertical velocity or temperature and lightning are limited by local climate characteristics and differences in convective processes. The proxy radar reflectivity driven by lightning data can also be assimilated [34][35][36][37]. However, not only are there errors in the proxy radar reflectivity from lightning data, but the radar data assimilation system itself has many shortcomings.…”

Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

“…Finally, the third type is based on the empirical relationship between lightning and radar reflectivity. The flash density is converted into three-dimensional radar reflectivity fields, which are then assimilated into the model [44][45][46][47].…”