Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances

By Wan Wu, R. Purser, David F. Parrish. 2002

687 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/255bf7c5a5956ccb467bf124649aa43d2baf98c1

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Citations in the corpus

The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model (2006, methodology): “This can be also useful for future evaluation of heterogeneous covariance formulations, such as those based on wavelets (Fisher 2003; Deckmyn and Berre 2005) or recursive filters (Wu et al. 2002).”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, methodology): “Techniques are also being developed to include some spatial inhomogeneity and anisotropy in the standard covariance models used in 3DVAR (e.g., Desroziers 1997; Riishøjgaard 1998; Purser et al. 2003; Wu et al. 2002).”
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). (2017, methodology): “The key components of the system are the GEOS atmospheric model (Rienecker et al. 2008; Molod et al. 2015) and the GSI analysis scheme (Wu et al. 2002; Kleist et al. 2009b).”
Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting (2005, neutral): “Several approaches for efficiently representing the background-error correlations without these assumptions have been proposed in the literature (e.g. Derber and Rosati 1989; Desroziers 1997; Weaver and Courtier 2001; Wu et al. 2002).”
Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System (2008, methodology): “…model and exact adjoint of the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis scheme (Wu et al. 2002) are developed and tested in the context of the Goddard Earth Observing System atmospheric data assimilation system (GEOS DAS;…”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, background): “…dynamics (Lin, 2004) and Goddard-developed physics; the adjoint of the finite-volume dynamics (Giering et al., 2005) which accounts for simple vertical diffusion; the Grid-point Statistical Interpolation (GSI) analysis system (Wu et al., 2002); and the adjoint of the GSI (Trémolet, 2007b).”
A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars (2004, methodology): “Using the recursive filter is a simple and efficient way to spread the effect of each radar observation to the analyzed grid points (Wu et al. 2002).”
GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments (2013, methodology): “statistical interpolation system (GSI; Wu et al. 2002; Kleist et al. 2009b) adopted by NCEP, use a background error covariance matrix that is either completely static or only weakly coupled to the dynamics of the forecast”
The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation. (2017, methodology): “2015) and the Three-dimensional Variational Data Analysis (3DVAR) Gridpoint Statistical Interpolation (GSI) meteorological analysis scheme (Wu et al. 2002; Kleist et al. 2009).”
The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. (2017, methodology): “…assimilation method) can be found in Part I. Briefly, MERRA-2 uses the GEOS-5 Earth system model (Rienecker et al. 2008; Molod et al. 2015) and the three-dimensional variational data assimilation (3DVar) Gridpoint Statistical Interpolation analysis system (GSI) (Wu et al. 2002; Kleist et al. 2009).”
Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis (2014, methodology): “GSI includes all the in situ and remotely sensed data used for operational weather prediction at NCEP.”
Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment (2017, methodology): “2006, 2008) and the Global Data Assimilation System (GDAS; Wu et al. 2002; Kleist et al. 2009a,b; Wang et al. 2013; Kleist and Ide 2015).”
Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework (2010, methodology): “Gridpoint statistical interpolation (GSI; Wu et al. 2002; Kleist et al. 2009), a three-dimensional variational data assimilation method (3DVAR) has been widely used in operations and research in numerical weather prediction (NWP).”
Enhanced radiance bias correction in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation data assimilation system (2014, background): “…Interpolation (SSI) data assimilation system (Derber et al., 1991; Parrish and Derber, 1992) and later in the Gridpoint Statistical Interpolation (GSI) system (Wu et al., 2002; Purser et al., 2003a, 2003b) at the National Centers for Environmental Prediction (NCEP) by Derber and Wu (1998).”
A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment (2008, methodology): “The structure of the WRF 3DVAR NCEP background-error covariances is similar to that described in Wu et al. (2002) .”