Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA

Autor: Kristin M. Calhoun, William Y. Y. Cheng, Alexandre O. Fierro, Yuewei Liu, Tianliang Zhao, Mei Xu, Si Shen, Yubao Liu, Haoliang Wang
Rok vydání: 2017
Předmět:
Zdroj: Journal of Geophysical Research: Atmospheres. 122:12-12,316
ISSN: 2169-897X
Popis: In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the NCAR (National Center for Atmospheric Research) Weather Research and Forecasting – Real-Time Four-Dimensional Data assimilation (WRF-RTFDDA) system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid-boxes, column-integrated graupel mass is first calculated using an observation-based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short-term (0 – 6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short-term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool and front locations. The improvements were most notable in the first two hours, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0 – 2 h) applications.
Databáze: OpenAIRE
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