scholarly journals Comparing Bayesian Model Averaging and Reliability Ensemble Averaging in Post-Processing Runoff Projections under Climate Change

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2124
Author(s):  
Kai Duan ◽  
Xiaola Wang ◽  
Bingjun Liu ◽  
Tongtiegang Zhao ◽  
Xiaohong Chen
Keyword(s):  
Parameter Estimation ◽  
Em Algorithm ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Equal Weight ◽  
Post Processing ◽  
Ensemble Averaging ◽  
Weight Distributions ◽  
Model Ensembles

This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological models and climate downscaling models. The performance and weight distributions of five model ensembles were thoroughly compared, including simple equal-weight averaging, BMA, and REAs optimizing mean (REA-mean), maximum (REA-max), and minimum (REA-min) monthly runoff. The results suggest that REA and BMA both can synthesize individual models’ diverse skills with comparable reliability, despite of their different averaging strategies and assumptions. While BMA weighs candidate models by their predictive skills in the baseline period, REA also forces the model ensembles to approximate a convergent projection towards the long-term future. The type of incorporation of the uncertain future climate in REA weighting criteria, as well as the differences in parameter estimation (i.e., the expectation maximization (EM) algorithm in BMA and the Markov Chain Monte Carlo sampling method in REA), tend to cause larger uncertainty ranges in the weight distributions of REA ensembles. Moreover, our results show that different averaging objectives could cause much larger discrepancy than that induced by different weighting criteria or parameter estimation algorithms. Among the three REA ensembles, REA-max most resembled BMA because the EM algorithm of BMA converges to the minimum aggregated error, and thus emphasize the simulation of high flows. REA-min achieved better performance in terms of inter-annual temporal pattern, yet at the cost of compromising accuracy in capturing mean behaviors. Caution should be taken to strike a balance among runoff features of interest.

scholarly journals Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging

2021 ◽  
Vol 893 (1) ◽  
pp. 012028
Author(s):  
Robi Muharsyah ◽  
Dian Nur Ratri ◽  
Damiana Fitria Kussatiti
Keyword(s):  
Surface Temperature ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Seasonal Forecast ◽  
Sea Surface ◽  
Post Processing ◽  
Enso Events ◽  
Prediction Quality

Abstract Prediction of Sea Surface Temperature (SST) in Niño3.4 region (170 W - 120 W; 5S - 5N) is important as a valuable indicator to identify El Niño Southern Oscillation (ENSO), i.e., El Niño, La Niña, and Neutral condition for coming months. More accurate prediction Niño3.4 SST can be used to determine the response of ENSO phenomenon to rainfall over Indonesia region. SST predictions are routinely released by meteorological institutions such as the European Center for Medium-Range Weather Forecasts (ECMWF). However, SST predictions from the direct output (RAW) of global models such as ECMWF seasonal forecast is suffering from bias that affects the poor quality of SST predictions. As a result, it also increases the potential errors in predicting the ENSO events. This study uses SST from the output Ensemble Prediction System (EPS) of ECMWF seasonal forecast, namely SEAS5. SEAS5 SST is downloaded from The Copernicus Climate Change Service (C3S) for period 1993-2020. One value representing SST over Niño3.4 region is calculated for each lead-time (LT), LT0-LT6. Bayesian Model Averaging (BMA) is selected as one of the post-processing methods to improve the prediction quality of SEAS5-RAW. The advantage of BMA over other post-processing methods is its ability to quantify the uncertainty in EPS, which is expressed as probability density function (PDF) predictive. It was found that the BMA calibration process reaches optimal performance using 160 months training window. The result show, prediction quality of Niño3.4 SST of BMA output is superior to SEAS5-RAW, especially for LT0, LT1, and LT2. In term deterministic prediction, BMA shows a lower Root Mean Square Error (RMSE), higher Proportion of Correct (PC). In term probabilistic prediction, the error rate of BMA, which is showed by the Brier Score is lower than RAW. Moreover, BMA shows a good ability to discriminating ENSO events which indicates by AUC ROC close to a perfect score.

scholarly journals Reconstructed Regional Mean Climate with Bayesian Model Averaging: A Case Study for Temperature Reconstruction in the Yunnan–Guizhou Plateau, China

2016 ◽  
Vol 29 (14) ◽  
pp. 5355-5361 ◽  
Author(s):  
Xianliang Zhang ◽  
Xiaodong Yan ◽  
Zhenju Chen
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Temperature Reconstruction ◽  
Arithmetic Mean ◽  
Equal Weight ◽  
Simple Arithmetic ◽  
Mean Temperature ◽  
Reconstructed Temperature ◽  
Guizhou Plateau

Abstract A method was developed, based on Bayesian model averaging (BMA), to reconstruct regional mean temperature. Different from the arithmetic mean, which gives equal weight to each chronology, BMA weights the chronologies according to their contributions to the actual temperature variances. Thus, BMA holds advantages in integrating chronologies to reconstruct the regional mean temperature. The regional mean temperature for the Yunnan–Guizhou Plateau was reconstructed for the past four centuries (1628–2005) using BMA, which performed better than the simple arithmetic mean. The reconstruction explained 41.33% of total observed temperature variances during the period of 1961–2005. The warmest decade was found to be 1840–50 and the coldest 1810–20 prior to the instrumental period. The reconstructed temperature showed a high correlation (r > 0.7, p < 0.001) with gridded observed temperatures in most grid cells of the Yunnan–Guizhou Plateau, suggesting that the regional temperature changes were captured well by the reconstruction.

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

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

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