A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes

By Xuguang Wang, C. Bishop. 2003

431 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/46383887576f82e5959b63cc08376c2d33b3f48b

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Citations in the corpus

Data Assimilation in the ADAPT Photospheric Flux Transport Model (2014, neutral): “The photospheric observations often fall far enough away from the entire ADAPT ensemble that, even with localization of the ETKF data assimilation scheme, observations are discarded by ADAPT and simulations diverge from observations [41].”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, methodology): “In a more realistic data assimilation system with a much large number of available observations, Wang and Bishop (2003) have shown the “observed” inflation”
GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments (2013, methodology): “…was found in Whitaker et al. (2008), where the EnKF was tested in GDAS at T62 resolution assimilating only conventional observations and in Wang et al. (2008b), where the ETKF (Wang and Bishop 2003; Wang et al. 2004, 2007a) was used to produce the ensemble for the WRFVar-based hybrid system.”
A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending (2014, methodology): “2008) and the ensemble transform Kalman filter (ETKF; Bishop et al. 2001; Wang and Bishop 2003).”
A new measure of ensemble performance: Perturbation versus error correlation analysis (PECA) (2003, result): “This conjecture is corroborated by the results of Wang and Bishop (2002) , who found that the use of the ensemble transform Kalman filter technique (ETKF; Bishop et al. 2001) for rescaling and orthogonalizing ensemble perturbations improved the performance of a bred ensemble.”
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, methodology): “Other efficient adaptive inflation methods have been proposed by, e.g., Wang and Bishop (2003), Anderson (2007), Li et al. (2009), Zheng (2009), Brankart et al. (2010), Miyoshi (2011), Liang et al. (2012), and Ying and Zhang (2015) for broader uses including extrinsic model error.”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, methodology): “This is a very well-known result that has been used in many atmospheric and oceanographic DA studies (e.g., Wang and Bishop 2003; Wang et al. 2007; Li et al. 2009; Miyoshi 2011; Karspeck et al. 2013).”
Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging (2007, background): “The remaining 14 bred members can be assumed to have identically distributed (but not independent; see Wang and Bishop 2003) errors and hence should have been assigned equal weights.”
Improvement of ensemble reliability with a new dressing kernel (2005, methodology): “To check the performance of the best-memberdressing andthe newdressingmethodsin the nonlinearsystemwith non-Gaussianerrorstatistics, we apply both dressing methods to the 1- to 10-day CCM3 ETKF nonlinearatmospheric ensemble forecasts (Bishop et al. 2001; Wang and Bishop 2003; Wang et al. 2004).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “Hence, adaptive schemes have been developed to make the task more automatic (Wang and Bishop, 2003; Anderson, 2007; Li et al., 2009; Zheng , 2009; Brankart et al., 2010; Bocquet , 2011; Miyoshi , 2011; Bocquet and Sakov , 2012; Liang et al., 2012; Ying and Zhang , 2015; Bocquet et al., 2015b).”
Early Warnings of Severe Weather from Ensemble Forecast Information (2004, background): “2004) or the Ensemble Transform Kalman Filter (Bishop et al. 2001; Wang and Bishop 2003), and some of these may prove more effective for short-range prediction of severe weather.”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, methodology): “These biases may be due to model errors, insufficient resolution (Weisman et al. 1997; Mullen and Buizza 2002; Szunyogh and Toth 2002; Buizza et al. 2003) or suboptimal parameteriza- tions, suboptimal methods for generating the initial conditions (Barkmeijer et al. 1998, 1999; Hamill et al.”
Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance (2014, background): “ensemble members do not represent initial-condition uncertainty (Hamill et al. 2003; Wang and Bishop 2003).”
Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, background): “…area of research for ensemble forecasting (Stensrud et al. 2000; Hou et al. 2001; Richardson 2001; Wandishin et al. 2001; Alhamed et al. 2002; Wang and Bishop 2003; Nutter et al. 2004; Wang et al. 2004; Yussouf et al. 2004; Eckel and Mass 2005; Gallus and Bresch 2006; Aligo et al. 2007;…”
An analog ensemble for short-term probabilistic solar power forecast (2015, background): “, bins) of spread, instead of considering its overall average [44–46].”