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RISProbabilistic Visibility Forecasting Using Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: ayesian model averaging (BMA) is a statistical postprocessing technique that has been used in probabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs) ...
Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a ...
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
Publisher: American Meteorological Society
Abstract: Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian ...
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
Publisher: American Meteorological Society
Abstract: Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output ...
Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts
Publisher: American Meteorological Society
Abstract: Forecast ensembles typically show a spread?skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles ...
Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: Bayesian model averaging (BMA) has recently been proposed as a way of correcting underdispersion in ensemble forecasts. BMA is a standard statistical procedure for combining predictive distributions from different sources. ...
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Publisher: American Meteorological Society
Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: robabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike ...
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: he authors introduce two ways to produce locally calibrated grid-based probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has ...
Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
Publisher: American Meteorological Society
Abstract: Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted ...