A Comparison of Probabilistic Forecasts from Bred, Singular-Vector, and Perturbed Observation Ensembles

By Thomas M. Hamill, C. Snyder, Rebecca Morss. 2000

178 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/cf3f0ec682cecd8d5111efedd43f9720ec746e7a

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Citations in the corpus

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2 (2013, methodology): “The ROC has gained widespread acceptance as a tool for probabilistic as well as ensemble forecasts verification (Wilks 2005; Mason and Graham 1999; Hamill et al. 2000).”
A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes (2003, background): “Hamill et al. (2000) further studied the ensemble spread skill of these three ensemble generation schemes.”
Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill (2011, background): “As pointed out by Hamill et al. (2000), initial condition perturbations added to the EPS should produce realistic samples of the forecast PDF.”
Using Bayesian Model Averaging to Calibrate Forecast Ensembles (2005, background): “and Petroliagis 1996; Houtekamer and Derome 1995; Hamill, Snyder, and Morss 2000 ).”
Ensemble forecasting (2008, methodology): “The authors in [51] also used a model of the large-scale, quasi-geostrophic dynamics in the extra-tropics.”
Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter (2001, methodology): “The forecast model was described in Zou et al. (1993). The model state vector consists of vorticity and divergence spectra at two levels as well as Exner function p at the lower surface and at an interface.”
Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations (2002, methodology): “Previous work has shown that in a perfect-model context, such ensembles have desirable sampling characteristics when used either with a 3DVAR assimilation (Hamill et al. 2000) or in the context of the EnKF (Houtekamer and Mitchell 1998, 2001; Hamill and Snyder 2000; Hamill et al. 2001).”
Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants (2007, background): “To understand such effects, our work delineates the consequences of matrix sample variability in two enKf variants: the perturbed observations scheme (Evensen and van Leeuwen, 1996; Hamill et al., 2000) and the square-root enKf (Whitaker and Hamill, 2002; Tippett et al., 2003).”
Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “In practice, the initial ensemble members are chosen in ways (Hamill et al. 2000, 2003; Wang & Bishop 2003) that do not randomly sample from the PDF of initial-condition uncertainty.”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, background): “Because operational ensembles are not optimized for this application (Hamill et al. 2000, 2003; Wang and Bishop 2003), alternative ensemble generation schemes may need to be explored.”
Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions (2011, methodology): “The characteristics and performance of ensembles generated by these different techniques have been well studied and compared (Descamps and Talagrand 2007; Buizza et al. 2005; Hamill et al. 2000).”