Real-Time Low-Level Wind and Temperature Analysis Using Single WSR-88D Data

By Juanzhen Sun, N. A. Crook. 2001

176 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computational Mechanics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b10c3bcc586fc40fb90596417828983f2b8b4c6a

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Citations in the corpus

Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses (2011, methodology): “Data from each elevation angle in each volume were objectively analyzed to grid points on the conical scan surfaces (Sun and Crook 2001; Dowell et al. 2004b; Dowell and Wicker 2009).”
The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region (2011, background): “Analysis and nowcasting of nearsurface temperature (Sun and Crook 2001) or wind (Crook and Sun 2004) have mainly been regarded as a means for predicting convective initiation and the development of deep convection (Wilson and Schreiber 1986; Wilson et al. 2004).”
Wind and Temperature Retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: Ensemble Kalman Filter Experiments (2004, methodology): “Instead, we applied an objective analysis to each sweep separately (Sun and Crook 2001), thereby preserving the conical distribution of the data and reducing the potential for vertical interpolation/extrapolation errors.”
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, methodology): “…(elevation angle) are first objectively analyzed separately using a 2-km Cressman radius of influence; thus, the preprocessed observations still lie on their respective original sweep surfaces, but are interpolated horizontally onto a regular 2-km grid (Sun and Crook 2001; Dowell et al. 2004a).”
Convective‐scale assimilation of radar data: progress and challenges (2005, methodology): “Sci., 52, 1265–1287 Snyder, C. and Zhang, F. 2004 Assimilation of simulated Doppler radar observations with an ensemble Kalman filter.”
The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation (2003, methodology): “While four-dimensional variational (4DVAR) data assimilation is generally considered superior and considerable successes have been achieved in applying 4DVAR to operational systems (e.g., Rabier et al, 2000) and to small-scale applications with radar data (e.g., Sun and Crook, 2001; Gao et al, 199”
The relationship between lightning activity and ice fluxes in thunderstorms (2008, methodology): “[23] The divergence herein was calculated using the horizontal wind components from the dual-Doppler synthesis or VDRAS.”
Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, methodology): “We objectively analyze each radar sweep separately to grid points on the conical scan surfaces (Sun and Crook 2001; Dowell et al. 2004a).”
Improving Parameterization of Rain Microphysics with Disdrometer and Radar Observations (2006, background): “Its exact form can be found in Sun and Crook (2001).”