Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter

By C. Snyder, Fuqing Zhang. 2003

527 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8e1bec5c8a5ca5f95d7371a13e66ac33330e8bd5

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

In Global and Planetary Change

Citations in the corpus

Ensemble-Based Sensitivity Analysis (2008, methodology): “…ensembles to underestimate covariance magnitude is treated by inflating the deviations from the ensemble mean by replacing the posterior perturbations with a linear combination of the prior and posterior perturbations where the prior (posterior) is weighted by 0.80 (0.20) (Snyder and Zhang 2003).”
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).”
Convective‐scale assimilation of radar data: progress and challenges (2005, methodology): “Snyder and Zhang (2004) and Zhang, F. et al. (2004) investigated the capability of a square-root EnKF in retrieving the unobserved model variables with identical-twin experiments using simulated single-Doppler observations.”
The growing impact of satellite observations sensitive to humidity, cloud and precipitation (2017, background): “…(e.g. Hunt et al., 2007) that can be used for cloud and precipitation assimilation without the need to develop TL and adjoint models (e.g. Snyder and Zhang, 2003; Otkin, 2010; Zhang et al., 2013; Lien et al., 2016) does not circumvent the difficulty of dealing with nonlinear and…”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, methodology): “Briefly, the sea-breeze model is hydrostatic, nonrotational, and incompressible.”
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): “EnKF performance in the Caya et al. (2005) experiments was comparable to that obtained by Snyder and Zhang (2003) experiments, in which only velocity observations were assimilated.”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, background): “Another problem with respect to HR-NWP is the distinct nonlinear character of the relevant microphysical processes (Snyder and Zhang 2003).”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, background): “…model error is likely to cause data assimilation problems if the model error does not grow in proportion to the background spread, as covariance inflation assumes [see also the discussion of inflation problems for convective-scale data assimilation in Snyder and Zhang (2003) their section 6b].”
Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research (2009, background): “…radars], continually increasing computing power, used not just for simulations but also to improve diagnoses of storm morphology via data assimilation (Snyder and Zhang, 2003; Dowell et al., 2004; Zhang and Snyder, 2007), and a growing interest in severe convective storms worldwide.”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, methodology): “Kkþ1 1⁄4 S kþ1jk S yy kþ1jk þ Rkþ1 h i 1 ð7Þ Here Sk+1jk xy is the cross covariance of the forecasted states xk+1jk i and the forecasted output h(xk+1jk i , q) [Snyder and Zhang, 2003], and it is approximated as follows:”
Sampling Errors in Ensemble Kalman Filtering. Part I: Theory (2008, background): “Deterministic formulations of Monte Carlo– based techniques, as reviewed by, for example, Tippett et al. (2003), do not require perturbed observations.”
Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter (2004, unknown): “q 2004 American Meteorological Society”
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).”
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, background): “…and reflectivity observations, the statistics explained in this section are computed only at locations where observed reflectivity is greater than 15 dBZ, in order to isolate the measurement of performances to the main convective cores of observed systems (Snyder and Zhang 2003; Tong and Xue 2005).”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, background): “…(e.g. Houtekamer et al., 2005), and from globalscale (e.g. Whitaker et al., 2008; Szunyogh et al., 2008) to regional-scale models (e.g. Snyder and Zhang, 2003; Zhang et al., 2006; Torn et al., 2006), due to their ease of implementation and the flow-dependent background error…”
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.”
Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges (2014, methodology): “The EnKF DA method was applied to the convective-scale radar DA initially by Snyder and Zhang (2003) for an " identical twin " problem with a perfect prediction model and simulated radial velocity observations, after promising results were shown by Evensen (1994) and Houtekamer and Mitchell (1998)…”