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42
Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133
, 2005
"... Ensembles used for probabilistic weather forecasting often exhibit a spreaderror correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distr ..."
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Cited by 71 (28 self)
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Ensembles used for probabilistic weather forecasting often exhibit a spreaderror correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF) of any quantity of interest is a weighted average of PDFs centered on the individual biascorrected forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts and reflect the models ’ relative contributions to predictive skill over the training period. The BMA weights can be used to assess the usefulness of ensemble members, and this can be used as a basis for selecting ensemble members; this can be useful given the cost of running large ensembles. The BMA PDF can be represented as an unweighted ensemble of any desired size, by simulating from the BMA predictive distribution. The BMA predictive variance can be decomposed into two components, one corresponding to the betweenforecast variability, and the second to the withinforecast variability. Predictive PDFs or intervals based solely on the ensemble spread incorporate the first component but not the second. Thus BMA provides a theoretical explanation of the tendency of ensembles to exhibit a spreaderror correlation but yet
A Hybrid Ensemble Kalman Filter / 3DVariational Analysis Scheme
"... A hybrid 3dimensional variational (3DVar) / ensemble Kalman filter analysis scheme is demonstrated using a quasigeostrophic model under perfectmodel assumptions. Four networks with differing observational densities are tested, including one network with a data void. The hybrid scheme operates by ..."
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Cited by 60 (15 self)
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A hybrid 3dimensional variational (3DVar) / ensemble Kalman filter analysis scheme is demonstrated using a quasigeostrophic model under perfectmodel assumptions. Four networks with differing observational densities are tested, including one network with a data void. The hybrid scheme operates by computing a set of parallel data assimilation cycles, with each member of the set receiving unique perturbed observations. The perturbed observations are generated by adding random noise consistent with observation error statistics to the control set of observations. Background error statistics for the data assimilation are estimated from a linear combination of timeinvariant 3DVar covariances and flowdependent covariances developed from the ensemble of shortrange forecasts. The hybrid scheme allows the user to weight the relative contributions of the 3DVar and ensemblebased background covariances. The analysis scheme was cycled for 90 days, with new observations assimilated every 12 h...
Probabilistic forecasts, calibration and sharpness
 Journal of the Royal Statistical Society Series B
, 2007
"... Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive dis ..."
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Cited by 38 (15 self)
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Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions and is a property of the forecasts only. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with crossvalidation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.
2000: A comparison of probabilistic forecasts from bred, singularvector, and perturbation observation ensembles
 Mon. Wea. Rev
"... The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3Dvariational data assimilation scheme. A perfect model is assumed. Three perturbation methodologies are considere ..."
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Cited by 33 (6 self)
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The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3Dvariational data assimilation scheme. A perfect model is assumed. Three perturbation methodologies are considered. The breeding and singularvector (SV) methods approximate the strategies currently used at operational centers in the United States and Europe, respectively. The perturbed observation (PO) methodology approximates a random sample from the analysis probability density function (pdf) and is similar to the method performed at the Canadian Meteorological Centre. Initial conditions for the PO ensemble are analyses from independent, parallel data assimilation cycles. Each assimilation cycle utilizes observations perturbed by random noise whose statistics are consistent with observational error covariances. Each member’s assimilation/forecast cycle is also started from a distinct initial condition. Relative to breeding and SV, the PO method here produced analyses and forecasts with desirable statistical characteristics. These include consistent rank histogram uniformity for all variables at all lead times, high spread/ skill correlations, and calibrated, reducederror probabilistic forecasts. It achieved these improvements primarily because 1) the ensemble mean of the PO initial conditions was more accurate than the mean of the bred or
Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation
 Monthly Weather Review
, 2005
"... Ensemble prediction systems typically show positive spreaderror correlation, but they are subject to forecast bias and underdispersion, and therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy to implement postprocessing technique that addresses b ..."
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Cited by 31 (9 self)
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Ensemble prediction systems typically show positive spreaderror correlation, but they are subject to forecast bias and underdispersion, and therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy to implement postprocessing technique that addresses both forecast bias and underdispersion and takes account of the spreadskill relationship. The technique is based on multiple linear regression and akin to the superensemble approach that has traditionally been used for deterministicstyle forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables, and can be applied to gridded model output. The EMOS predictive mean is an optimal, biascorrected weighted average of the ensemble member forecasts, with coefficients that are constrained to be nonnegative and associated with the member model skill. The EMOS predictive mean provides a highly accurate deterministicstyle forecast. The EMOS predictive variance is a linear function of the ensemble spread. For fitting the EMOS coefficients, the method of minimum CRPS estimation is introduced.
Disentangling Uncertainty and Error: On the Predictability of Nonlinear Systems
 Nonlinear Dynamics and Statistics
, 2000
"... Chaos places no a priori restrictions on predictability: any uncertainty in the initial condition can be evolved and then quanti ed as a function of forecast time. If a speci ed accuracy at a given future time is desired, a perfect model can specify the initial accuracy required to obtain it, and ac ..."
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Cited by 25 (6 self)
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Chaos places no a priori restrictions on predictability: any uncertainty in the initial condition can be evolved and then quanti ed as a function of forecast time. If a speci ed accuracy at a given future time is desired, a perfect model can specify the initial accuracy required to obtain it, and accountable ensemble forecasts can be obtained for each unknown initial condition. Statistics which reect the global properties of in nitesimals, such as Lyapunov exponents which de ne \chaos", limit predictability only in the simplest mathematical examples. Model error, on the other hand, makes forecasting a dubious endeavor. Forecasting with uncertain initial conditions in the perfect model scenario is contrasted with the case where a perfect model is unavailable, perhaps nonexistent. Applications to both low (2 to 400) dimensional models and high (10 7 ) dimensional models are discussed. For real physical systems no perfect model exists; the limitations of nearperfect models are consider...
Evaluation of a shortrange multimodel ensemble system
, 2001
"... Forecasts from the National Centers for Environmental Prediction’s experimental shortrange ensemble system are examined and compared with a single run from a higherresolution model using similar computational resources. The ensemble consists of five members from the Regional Spectral Model and 10 ..."
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Cited by 22 (3 self)
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Forecasts from the National Centers for Environmental Prediction’s experimental shortrange ensemble system are examined and compared with a single run from a higherresolution model using similar computational resources. The ensemble consists of five members from the Regional Spectral Model and 10 members from the 80km Eta Model, with both inhouse analyses and bred perturbations used as initial conditions. This configuration allows for a comparison of the two models and the two perturbation strategies, as well as a preliminary investigation of the relative merits of mixedmodel, mixedperturbation ensemble systems. The ensemble is also used to estimate the shortrange predictability limits of forecasts of precipitation and fields relevant to the forecast of precipitation. Whereas error growth curves for the ensemble and its subgroups are in relative agreement with previous work for largescale fields such as 500mb heights, little or no error growth is found for fields of mesoscale interest, such as convective indices and precipitation. The difference in growth rates among the ensemble subgroups illustrates the role of both initial perturbation strategy and model formulation in creating ensemble dispersion. However, increase spread per se is not necessarily beneficial, as is indicated by the fact that the ensemble subgroup with the greatest spread is less skillful than the subgroup with the least spread. Further examination into the skill of the ensemble system for forecasts of precipitation shows the advantage gained from a mixedmodel strategy, such that even the inclusion of the less skillful Regional Spectral Model members improves ensemble performance. For some aspects of forecast performance, even ensemble configurations with as few as five members are shown to significantly outperform the 29km MesoEta Model. 1.
2008: A comparison of precipitation forecast skill between small nearconvectionpermitting and large convectionparameterizing ensembles. Submitted to Weather and Forecasting
"... Abstract An experiment is designed to evaluate and compare precipitation forecasts from a 5member, 4km gridspacing (ENS4) and a 15member, 20km gridspacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble fo ..."
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Cited by 21 (18 self)
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Abstract An experiment is designed to evaluate and compare precipitation forecasts from a 5member, 4km gridspacing (ENS4) and a 15member, 20km gridspacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 hours. Previous work has demonstrated that simulations using convectionallowing resolution (CAR; dx ~ 4km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterizedconvection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 inches at forecast lead times of 9 to 21 hours (0600 – 1800 UTC) for all accumulation intervals
Evaluation of EtaRSM ensemble probabilistic precipitation forecasts
 Mon. Wea. Rev
, 1998
"... The accuracy of shortrange probabilistic forecasts of quantitative precipitation (PQPF) from the experimental Eta–Regional Spectral Model ensemble is compared with the accuracy of forecasts from the Nested Grid Model’s model output statistics (MOS) over a set of 13 case days from September 1995 thr ..."
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Cited by 16 (0 self)
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The accuracy of shortrange probabilistic forecasts of quantitative precipitation (PQPF) from the experimental Eta–Regional Spectral Model ensemble is compared with the accuracy of forecasts from the Nested Grid Model’s model output statistics (MOS) over a set of 13 case days from September 1995 through January 1996. Ensembles adjusted to compensate for deficiencies noted in prior forecasts were found to be more skillful than MOS for all precipitation categories except the basic probability of measurable precipitation. Gamma distributions fit to the corrected ensemble probability distributions provided an additional small improvement. Interestingly, despite the favorable comparison with MOS forecasts, this ensemble configuration showed no ability to ‘‘forecast the forecast skill’ ’ of precipitation—that is, the ensemble was not able to forecast the variable specificity of the ensemble probability distribution from daytoday and locationtolocation. Probability forecasts from gamma distributions developed as a function of the ensemble mean alone were as skillful at PQPF as forecasts from distributions whose specificity varied with the spread of the ensemble. Since forecasters desire information on forecast uncertainty from the ensemble, these results suggest that future ensemble configurations should be checked carefully for their presumed ability to forecast uncertainty. 1.