Xuanli Li
University of Alabama in Huntsville
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Publication
Featured researches published by Xuanli Li.
Monthly Weather Review | 2012
Xuanli Li; John R. Mecikalski
AbstractThe dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. In this study, the horizontal reflectivity ZH, differential reflectivity ZDR, specific differential phase KDP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. A warm-rain scheme is constructed to assimilate ZH, ZDR, and KDP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system.The results show that the ZH, ...
Monthly Weather Review | 2015
Derek J. Posselt; Xuanli Li; Samantha A. Tushaus; John R. Mecikalski
AbstractDual-polarization Doppler radar has proven useful for the estimation of hydrometeor content and the classification of hydrometeor type. Recent studies have leveraged dual-polarization-specific information to produce improved assimilated cloud and precipitation fields from the warm rain (above freezing) portion of deep convective storms. While the strengths of dual-polarization radar observations have been conclusively shown for rain and hail hydrometeors, it is less clear how much information is provided in mixed-phase and ice-only regions.In this paper, a Markov chain Monte Carlo (MCMC) algorithm is used to examine the information content of dual-polarization-specific variables in the ice-phase region of a convective storm. Results are used to quantify how much information is added by specific differential phase and radar correlation coefficient, as well as how this information is degraded when the assumed particle size distribution and particle density are allowed to vary. It is found that dual-...
Monthly Weather Review | 2017
Xuanli Li; John R. Mecikalski; Derek J. Posselt
AbstractIn this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase (KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on ho...
Journal of Geophysical Research | 2010
Xuanli Li; John R. Mecikalski
Journal of Atmospheric and Oceanic Technology | 2017
Kacie Hoover; John R. Mecikalski; Timothy J. Lang; Xuanli Li; Tyler Castillo; Themis Chronis
Archive | 2014
Bradley Zavodsky; Jayanthi Srikishen; Emily Berndt; Xuanli Li; Leela Watson
Archive | 2018
Xuanli Li; Jayanthi Srikishen; Bradley Zavodsky; John R. Mecikalski
Archive | 2018
Xuanli Li; Timothy J. Lang; John R. Mecikalski
Archive | 2017
Xuanli Li; Timothy J. Lang; John R. Mecikalski; Tyler Castillo; Kacie Hoover; Themis G. Chronis
Archive | 2017
Timothy J. Lang; Xuanli Li; Brent Roberts; John R. Mecikalski