3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR
By Ming Hu, Ming Xue, Jidong Gao, Keith A. Brewster. 2006
178 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/8f4f85feba6c7c7979df191515c30e30670f3f96
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Citations in the corpus
- Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification (2012, methodology): “The analysis vector x contains the three wind components u, y, and w; potential temperature u; pressure p; and water vapor mixing ratio qy; also, the hydrometer variables are analyzed in a cloud analysis scheme (Hu et al. 2006a).”
- Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses (2011, background): “based on reflectivity observations for convective storms that were roughly 50–200 km from the radar, Hu et al. (2006) also found that reflectivity observations were very”
- 3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part I: Cloud Analysis and Its Impact (2006, methodology): “In Part II (Hu et al. 2005), we will focus on the discussion of using radial velocity data via the 3DVAR procedure.”
- Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables (2008, background): “For Doppler weather radars like the Weather Surveillance Radar-1988 Doppler (WSR-88D), the radial velocity and equivalent radar reflectivity factor (here-after reflectivity) data are the two key measurements that can be assimilated into NWP models (e.g., Hu et al. 2006a,b).”