A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR
AbstractLightning network data, considered as a useful supplement to radar observations, are a good indicator of severe convection, and has high temporal and spatial resolution. In Numerical Weather Prediction (NWP) models, lightning data are a new source of data to improve the forecasting of convective systems. In this case study, lightning data assimilation is conducted by converting lightning data to water vapor mixing ratio via a simple smooth continuous function, with input variables of total flash rate and simulated graupel mixing ratio at 9 km gridded resolution. Relative humidity converted from the retrieved water vapor mixing ratio is assimilated into the background field utilizing the three-dimensional variational (3DVAR) method in WRFDA (the Weather Research and Forecasting model Data Assimilation system). The benefits of assimilating lightning data are demonstrated in a series of experiments using data from a strong convection event that affected Beijing, Tianjin, Hebei and Shandong Province, on 31 July 2007. A nested domain with resolutions of 9 km and 3 km is implemented. For this case, assimilating lightning data shows some improvements in predictions of both reflectivity and neighboring precipitation, and in the temperature, dew-point temperature and relative humidity profile after seven hours. View Full-Text
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Wang, Y.; Yang, Y.; Liu, D.; Zhang, D.; Yao, W.; Wang, C. A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR. Atmosphere 2017, 8, 55.
Wang Y, Yang Y, Liu D, Zhang D, Yao W, Wang C. A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR. Atmosphere. 2017; 8(3):55.Chicago/Turabian Style
Wang, Ying; Yang, Yi; Liu, Dongxia; Zhang, Dongbin; Yao, Wen; Wang, Chenghai. 2017. "A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR." Atmosphere 8, no. 3: 55.
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