A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results

By D. Barker, Wei Huang, Yong-run Guo, Alfred J. Bourgeois, Q. Xiao. 2004

915 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d89ae85210076de8826ddf863bc14c9feccab99a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. R. Bannister
2. Xuguang Wang
3. Fuqing Zhang
4. C. Snyder
5. Z. Meng
6. Juanzhen Sun
7. Q. Xiao
8. Thomas M. Hamill

In Global and Planetary Change

1. R. Bannister
2. Xuguang Wang
3. Fuqing Zhang
4. Z. Meng
5. Juanzhen Sun
6. C. Snyder
7. Q. Xiao
8. Thomas M. Hamill

Citations in the corpus

• A Description of the Advanced Research WRF Version 3 (2008, methodology): “The NMC-method code developed for MM5 3D-Var (Barker et al., 2004) is nearing the end”
• 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): “Its performance is directly compared with the three-dimensional variational data assimilation system of the WRF (WRF-3DVAR), complementary to similar comparisons performed with global models (Houtekamer et al. 2005; Whitaker et al. 2008).”
• On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, background): “The L03 and B05 framework should be easier to apply in model-space variational schemes where preconditioning is performed with respect to the background term (e.g., Parrish and Derber 1992; Lorenc et al. 2000; Gauthier et al. 1999; L03; Barker et al. 2004).”
• Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, methodology): “vective scale experiments in Zhang et al. (2004). Experiment “UONLY” differs from CNTL20 in that FIG.”
• Sensitivity of Numerical Simulation of Early Rapid Intensification of Hurricane Emily (2005) to Cloud Microphysical and Planetary Boundary Layer Parameterizations (2008, methodology): “The model initial conditions are generated by using a WRF three-dimensional variational data assimilation (3DVAR) system, which was developed at the National Center for Atmospheric Science (NCAR) based on the MM5 3DVAR system (Barker et al. 2004).”
• A Description of the Advanced Research WRF Model Version 4 (2019, methodology): “The WRFDA assimilation proceeds as described in Barker et al. (2004). A number of recent upgrades to the WRFDA algorithm will be described in Section 11.”
• Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method (2014, methodology): “The operators Up, Uy , and Uh are described in detail by Barker et al. (2004).”
• Convective‐scale assimilation of radar data: progress and challenges (2005, methodology): “Xiao et al. (2005) assimilated radar radial velocity from one of the Korean radars using the MM5 3D-Var system (Barker et al. 2004) with a 10 km resolution in a case-study of a mesoscale rain band.”
• A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “…matrices) that is (are) essentially the same for each cycle, although flow-dependence can be added artificially to the way that B0 is modelled by, for example, cycling variance information (Dee, 2002) and the use of (linearized) nonlinear balance relationships (Fisher, 2003; Barker et al., 2004).”
• A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, methodology): “This has been done by Fisher (2003) and Barker et al. (2004) (see Table III, row 1).”
• Air-Sea Enthalpy and Momentum Exchange at Major Hurricane Wind Speeds Observed during CBLAST (2012, neutral): “Manual analysis was largely superseded by the development of objective analysis techniques that created a gridded data field by weighting the observations by their distance from a particular grid point, such as the Barnes (1973) technique used in BM08.”
• Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 (2007, methodology): “The WRF 3DVAR originated and evolved from the MM5 3DVAR (Barker et al. 2004), but the basic software interface and coordinate framework are fully updated for the WRF-ARW model (Skamarock et al. 2005).”
• A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment (2008, methodology): “For details, please refer to Barker et al. (2004).”
• Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results (2009, methodology): “This paper documents the formulation and preliminary results of the 4D-Var capability developed as an extension to the current 3D-Var data assimilation system (Barker et al. 2004a,b).”
• Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events (2013, methodology): “In the WRF (Skamarock et al. 2008) model’s threedimensional variational data assimilation system (WRF 3D-Var; Barker et al. 2004, 2012), Xiao et al. (2007) developed a direct radar reflectivity data assimilation scheme.”