Probabilistic Forecast of Surface Air Temperature Using Bayesian Model Averaging

2019 ◽  
Vol 08 (03) ◽  
pp. 269-278
Author(s):  
红梅 周
Keyword(s):  
Air Temperature ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Surface Air Temperature ◽  
Probabilistic Forecast

scholarly journals Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

Agromet ◽  
2020 ◽  
Vol 34 (1) ◽  
pp. 20-33
Author(s):  
Robi Muharsyah ◽  
Tri Wahyu Hadi ◽  
Sapto Wahyu Indratno
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Probabilistic Forecast ◽  
Ensemble Prediction ◽  
Operating Characteristics ◽  
Ensemble Prediction System ◽  
Medium Range

Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.

scholarly journals A Probabilistic Forecast of Wind Speed using Bayesian Model Averaging

2016 ◽  
Vol 11 (3) ◽  
pp. 221-235
Author(s):  
Keunhee Han ◽  
◽  
Chansik Kim ◽  
Chansoo Kim
Keyword(s):  
Wind Speed ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Probabilistic Forecast

Probabilistic climate change predictions applying Bayesian model averaging

2007 ◽  
Vol 365 (1857) ◽  
pp. 2103-2116 ◽  
Author(s):  
Seung-Ki Min ◽  
Daniel Simonis ◽  
Andreas Hense
Keyword(s):  
Climate Change ◽  
Bayesian Model ◽  
Bayes Factor ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Surface Air Temperature ◽  
First Century ◽  
Intergovernmental Panel ◽  
Twenty First Century ◽  
Global Mean

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation–maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070–2099) SATs while there is only a little effect of Bayesian weighting on the 5–95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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