Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter

By Fuqing Zhang, C. Snyder, Juanzhen Sun. 2004

547 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a8044f9feb60da979e091bd8f396d2da2ea88d1b

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Citations in the corpus

An iterative ensemble Kalman smoother (2014, unknown): “These, along with the multiplicative inflation (Anderson and Anderson, 1999) applied at line 19 of Algorithm 1, include additive inflation (Houtekamer and Mitchell, 2005) and relaxation-toprior (Zhang et al., 2004).”
Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 (2007, methodology): “(2004) applied an ensemble Kalman filter technique (Evensen 1994; Snyder and Zhang 2003; Zhang et al. 2004) to initialize the cloud model developed by Sun and Crook (1997, 1998).”
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): “Another relatively new technique is the ensemble Kalman filter (EnKF) method, which has been shown recently to produce single-Doppler radar analyses of thunderstorms that are of similar quality as the 4DVAR analysis (Snyder and Zhang 2003; Zhang et al. 2004; Tong and Xue 2005).”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “…additive inflation (e.g., Whitaker et al. 2008; Houtekamer et al. 2009), where random perturbations are added to the analysis ensemble members, or the relaxation-to-prior method (Zhang et al. 2004), where the forecast ensemble perturbations are combined with the analysis ensemble perturbations.”
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, background): “Finally, if one chooses the analysis ensemble in such a way that each member has a corresponding member of the background ensemble, then one can inflate the analysis ensemble by “relaxation” toward the background ensemble: replacing each analysis perturbation from the mean by a weighted average of i”
Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “Zhang et al. (2004) proposed the relaxation to prior perturbation (RTPP) method, which blends the forecast and analysis ensemble perturbations.”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “It is also possible to relax the analysis ensemble spread closer to the spread of the forecast ensemble (Zhang et al., 2004).”
A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, methodology): “…contrary, ensemble Kalman filter (EnKF; cf. Evensen, 1994, 2004; Houtekamer and Mitchell, 1998; Keppenne, 2000; Anderson, 2001; Houtekamer andMitchell, 2001; Whitaker and Hamill, 2002; Ott et al., 2004; Zhang et al., 2004; Hunt et al., 2007) provides another advanced method for data assimilation.”
Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, methodology): “Nevertheless, some of the ad hoc storm-scale ensemble techniques that have been developed so far have proved to be useful for data assimilation (Snyder and Zhang 2003; Zhang et al. 2004; Dowell et al. 2004a,b; Caya et al. 2005; 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, background): “Snyder and Zhang (2003) and then Zhang et al. (2004) assimilated synthetic radial velocity observations into the Sun and Crook (1997) cloud model, with the intent of reproducing the supercell storm (Browning 1968) in a reference simulation from imperfect observations and a simplified initial state…”
Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments (2005, methodology): “Zhang et al. (2004) proposed another alternative to the traditional covariance inflation that is to avoid enhancing spurious cells but CSS found that the use of the method degrades the quality of analysis.”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “We distinguish the method pro358 posed in Zhang et al. (2004), where the forecast and analysis ensemble perturbations are blended, 359”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, methodology): “…ensemble data assimilation schemes (e.g., Evensen 1994; Burgers et al. 1998; Anderson 2001; Bishop et al. 2001; Whitaker and Hamill 2002, 2005; Snyder and Zhang 2003; Zhang et al. 2004; Ott et al. 2004; Szunyogh et al. 2005; Houtekamer and Mitchell 1998, 2001, 2005; Houtekamer et al. 2005).”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “These are (i) covariance, or multiplicative inflation (Anderson and Anderson 1999); (ii) additive inflation (Houtekamer and Mitchell 2005); and (iii) relaxation-to-prior (Zhang et al. 2004).”
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, background): “This is in agreement with previous EnKF studies that find ensemble sizes of 20–100 to be adequate across a range of applications (e.g., Mitchell et al. 2002; Zhang et al. 2004).”
Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study* (2015, methodology): “For example, in Part II of this series, the more robust relax-to-prior (RTP) scheme (Zhang et al. 2004) is used and satisfactory results are achieved without much tuning of the RTP factor.”
Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System (2014, background): “Recent studies have suggested that satellite radiance and Global Positioning System radio occultation data are significant in improving model forecasts (Zhu and Gelaro 2008; Cardinali 2009; Gelaro and Zhu 2009).”
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. 2003; Dowell et al. 2004; Hu et al. 2006; Smith et al. 2008; Weygandt et al. 2008; Dowell and Wicker 2009; Aksoy et al.…”