Interpretation of Rank Histograms for Verifying Ensemble Forecasts

By Thomas M. Hamill. 2001

908 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0599ebb8198d199c017d2dfaedb2c548763394c9

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Citations in the corpus

Effects of Coarsely Resolved and Temporally Interpolated Lateral Boundary Conditions on the Dispersion of Limited-Area Ensemble Forecasts (2004, background): “However, ensembles can be calibrated to remove such biases (Hamill and Colucci 1997; Hamill 2001).”
Wind and Temperature Retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: Ensemble Kalman Filter Experiments (2004, result): “Consistent with these results, rank histograms (Hamill 2001) tended to have a ‘‘U’’ shape (not shown).”
A method for statistical downscaling of seasonal ensemble predictions (2005, background): “A flat rank histogram indicates statistical consistency between forecasts and observations (but is not a sufficient condition; Hamill, 2001).”
Probabilistic Quantitative Precipitation Estimation in Complex Terrain (2005, neutral): “Hamill (2001) provides more details on the interpretation of rank histograms.”
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms (2013, background): “However, as dicussed by both Hamill (2001) and Gneiting et al. (2007), the use of formal tests is often complicated by the intricate dependence structures between the individual forecast cases.”
Estimating reliability and resolution of probability forecasts through decomposition of the empirical score (2012, background): “Several concepts of reliability are compared and contrasted in Hamill (2001).”
GRACE-REC: a reconstruction of climate-driven water storage changes over the last century (2019, background): “According to this first-order metric (see, e.g., Hamill, 2001, for a discussion), we conclude that regional averages of the ensemble members provide reliable forecasts (Fig.”
Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model (2015, background): “U-shaped rank histograms can indicate lack of variability in the ensemble, but can also be a sign of a combination of conditional biases in the model (Hamill, 2001).”
The minimum spanning tree histogram as a verification tool for multidimensional ensemble forecasts (2004, background): “As noted by Hamill (2001) in the context of scalar rank histograms, qualitative deviations from these synthetic results may occur for real forecasts, for example, when ensemble properties are not homogeneous within a particular sample of n forecasts.”
Combining Predictive Distributions (2011, background): “In general, auto-calibration is a much stronger requirement than probabilistic calibration, and there is a small but colorful, growing strand of recent literature that addresses its applied aspects for continuous outcomes (Hamill 2001; Mason et al. 2007; Held, Rufibach and Balabdaoui 2010; Bröcker, ”
Probabilistic forecasts, calibration and sharpness (2007, background): “ Hamill (2001) gave a thoughtful discussion of the associated issues and potential fallacies.”
A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes (2003, neutral): “Tests such as rank histograms (Hamill 2001), the relative operating characteristics (ROC) curve (Mason 1982; Richardson 2000), and the Brier Skill Score (Brier 1950; Atger 1999) will not be considered here because they require a PDF to be derived from the ensemble forecast and, in our view, the…”
Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, methodology): “The reliability of the predictive distribution can be visually assessed using the rank histogram (Talagrand et al., 1999; Hamill, 2001).”
Skilful precipitation nowcasting using deep generative models of radar (2021, methodology): “Rank Histograms We use rank histograms [44, 45] to measure calibration of ensemble forecasts of continuous rainfall values.”