Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments

By Mingjing Tong, Ming Xue. 2005

453 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/05e476b11d2498ce1fbadad786a1e6e5673aabbe

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In Atmospheric Science

1. Xuguang Wang
2. Juanzhen Sun
3. D. Dowell
4. C. Snyder
5. Ming Xue
6. Louis J. Wicker
7. Q. Xiao
8. Fuqing Zhang

In Global and Planetary Change

1. Xuguang Wang
2. Juanzhen Sun
3. D. Dowell
4. C. Snyder
5. Louis J. Wicker
6. Q. Xiao
7. Fuqing Zhang
8. Ming Xue

Citations in the corpus

• 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 (2006, methodology): “Tong and Xue (2005) found with observing system simulation experiments (OSSE) that radial velocity has a greater positive impact than reflectivity when assimilated using ensemble Kalman filter method for a simulated supercell storm.”
• Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study (2008, methodology): “Since the first application of an ensemble Kalman filter (EnKF; Evensen 1994) in the atmospheric sciences field (Houtekamer and Mitchell 1998), the EnKF has been widely examined with different models at different scales and to different realistic extents (Houtekamer and Mitchell 1998; Hamill and Sny”
• CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 (2009, background): “Several other studies have assimilated simulated (Zhang et al. 2004; Tong and Xue 2005; Caya et al. 2005; Xue et al. 2006; Jung et al. 2008) or real radar observations (Xue et al.”
• 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): “They also should take into account other effects that are necessary for realistic observations, such as the earth curvature effect or the radar beam pattern (Tong and Xue 2005a; Xue et al. 2006).”
• Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification (2012, methodology): “The ensemble Kalman filter (EnKF) is another approach for directly assimilating reflectivity data into an NWP model (Tong and Xue 2005; Dowell et al. 2011).”
• Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 (2007, methodology): “Tong and Xue (2005) used a similar technique for assimilation of Doppler radar data with a compressible nonhydrostatic model.”
• Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, methodology): “Assimilating low-reflectivity (nonprecipitation) observations in the storm environment, which was not attempted here, could suppress the spurious cells (Dowell et al. 2004a; Tong and Xue 2005; Aksoy et al. 2009).”
• A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, methodology): “…to analysis quality of radar observations near the surface (Zhang et al. 2004), the assimilation of reflectivity in addition to radial velocity (Tong and Xue 2005; Caya et al. 2005), the comparison of the EnKF with 4DVAR (Caya et al. 2005), * Current affiliation: Cooperative Institute for…”
• 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 our updated cloud analysis, the three equations that define the contributions of rain, snow, and hail to the total reflectivity is based on Smith et al. (1975), and the exact form used can be found in Tong and Xue (2005).”
• 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): “After some experimentation, we chose a minimum ZdB threshold of 0 dBZ for our experiments, as did Tong and Xue (2005).”
• Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events (2013, background): “…the EnKF’s application to the convective scale was shown by assimilating simulated or real reflectivity observations (e.g., Dowell et al. 2004, 2011; Tong and Xue 2005, 2008; Xue et al. 2006; Jung et al. 2008; Aksoy et al. * The National Center for Atmospheric Research is sponsored by the National…”
• The Impact of Size Sorting on the Polarimetric Radar Variables (2012, background): “Many storm-scale data assimilation studies focus on such DMCS, both in observing system simulation experiments (OSSEs) (e.g., Snyder and Zhang 2003; Tong and Xue 2005; Jung et al. 2008; Yussouf and Stensrud 2012) as well as real-data experiments (e.g., Hu et al. 2006a,b; Aksoy et al. 2009; Lim and…”
• Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges (2014, methodology): “The ability of the EnKF in accurately analyzing microphysical species associated with a multiphase ice scheme, and in assimilating reflectivity observations, was first demonstrated by Tong and Xue (2005) using a fully compressible cloud model and simulated radar observations.”
• Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System (2014, result): “Some previous studies (e.g., Snyder and Zhang 2003; Zhang et al. 2004; Tong and Xue 2005) demonstrated the potential of radar data and surface observations for convective-scale analysis in the EnKF, producing promising results.”
• A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment (2008, unknown): “Studies more recently have included limited-area models resolving meso- and convective scales (e.g., Snyder and Zhang 2003; Dowell et al. 2004; Tong and Xue 2005; Meng and Zhang 2008; Liu et al. 2008; Dirren et al. 2007; Torn and Hakim 2008).”
• Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model (2014, background): “However, the assimilation of radar reflectivity on top of radial velocity only brings small improvements (Tong and Xue 2005; Caya et al. 2005).”
• Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, methodology): “... (1994), ensemble-based data assimilation has been implemented in numerous models to various realistic extents for different scales of interest (Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Keppenne 2000; Anderson 2001; Mitchell et al. 2002; Keppenne and Rienecker 2003; Zhang and Anderso”
• Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell (2017, methodology): “To illustrate these methods, the radar reflectivity observation operator consistent with the WSM6 microphysics scheme (Hong and Lim 2006) is used following Lin et al. (1983), Gilmore et al. (2004), Tong and Xue (2005), Dowell et al. (2011), and Johnson et al. (2015).”
• Simulations of Polarimetric Radar Signatures of a Supercell Storm Using a Two-Moment Bulk Microphysics Scheme (2010, background): “2 of Tong and Xue (2005), which used the same Del City sounding.”