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

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.

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Create the ensemble sea surface temperature using the Bayesian model averaging

10.1117/12.2067324 ◽

2014 ◽

Author(s):

Kwangjin Kim ◽

Yang-Won Lee

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Sea Surface

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Human Capital and Economic Growth in Morocco: Evidence from Bayesian Model Averaging

International Business Research ◽

10.5539/ibr.v10n12p167 ◽

2017 ◽

Vol 10 (12) ◽

pp. 167 ◽

Cited By ~ 1

Author(s):

Omar Essardi ◽

Redouane Razzouk

Keyword(s):

Economic Growth ◽

Human Capital ◽

Life Expectancy ◽

Granger Causality ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Granger Causality Test ◽

Causality Test

The paper investigates the relationship between human capital and economic growth in Morocco during the period from 1965 to 2015. In order to test this relationship we estimated a growth function using firstly the Johansen multivariate cointegration test and the Granger causality test. Secondly, we used the method of the Bayesian Model Averaging (BMA) that takes into consideration the uncertainty related to the specification of the model studied. In the theoretical literature, the difficulty of measuring human capital is often stressed. In order to overcome this problem, we use four proxies of human capital: first, we employ the average years of schooling. Second, we use the index of the gap in life expectancy between Morocco and developed countries. Third, we integrate the qualitative aspects of education and health by constructing two composite indicators of human capital using Principal Component Analysis (PCA) method.The main results of regression analysis confirm that in the specification of determinants of GDP per worker the average years of total schooling, the life expectancy index and the indicator of quality of health affect positively and significantly level of GDP per worker. However, in the specification of determinants of the growth of the GDP per worker, we found there is no proxy of human capital that affects significantly the growth of the GDP per worker.In addition, the results of Granger causality test show that only the indicator of quality of health that cause the GDP per worker. As well, these results show that the average years of total schooling and the indicator of quality of education cause the growth of GDP per worker. We suggest that the Moroccan authorities should make additional efforts to raise the level of quality of human capital especially in the health sector and increase the productivity of both public and private investment.

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A Bayesian model averaging approach to examining changes in quality of life among returning Iraq and Afghanistan Veterans

International Journal of Methods in Psychiatric Research ◽

10.1002/mpr.1442 ◽

2014 ◽

Vol 23 (3) ◽

pp. 345-358 ◽

Cited By ~ 3

Author(s):

Eileen M. Stock ◽

Nathan A. Kimbrel ◽

Eric C. Meyer ◽

Laurel A. Copeland ◽

Ralph Monte ◽

...

Keyword(s):

Quality Of Life ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging

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Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging

Monthly Weather Review ◽

10.1175/mwr3347.1 ◽

2007 ◽

Vol 135 (4) ◽

pp. 1364-1385 ◽

Cited By ~ 76

Author(s):

Laurence J. Wilson ◽

Stephane Beauregard ◽

Adrian E. Raftery ◽

Richard Verret

Keyword(s):

Surface Temperature ◽

Bias Correction ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Training Sample ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

Modest Improvement ◽

Component Models

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. The output of BMA is a probability density function (pdf), which is a weighted average of pdfs centered on the bias-corrected forecasts. The BMA weights reflect the relative contributions of the component models to the predictive skill over a training sample. The variance of the BMA pdf is made up of two components, the between-model variance, and the within-model error variance, both estimated from the training sample. This paper describes the results of experiments with BMA to calibrate surface temperature forecasts from the 16-member Canadian ensemble system. Using one year of ensemble forecasts, BMA was applied for different training periods ranging from 25 to 80 days. The method was trained on the most recent forecast period, then applied to the next day’s forecasts as an independent sample. This process was repeated through the year, and forecast quality was evaluated using rank histograms, the continuous rank probability score, and the continuous rank probability skill score. An examination of the BMA weights provided a useful comparative evaluation of the component models, both for the ensemble itself and for the ensemble augmented with the unperturbed control forecast and the higher-resolution deterministic forecast. Training periods around 40 days provided a good calibration of the ensemble dispersion. Both full regression and simple bias-correction methods worked well to correct the bias, except that the full regression failed to completely remove seasonal trend biases in spring and fall. Simple correction of the bias was sufficient to produce positive forecast skill out to 10 days with respect to climatology, which was improved by the BMA. The addition of the control forecast and the full-resolution model forecast to the ensemble produced modest improvement in the forecasts for ranges out to about 7 days. Finally, BMA produced significantly narrower 90% prediction intervals compared to a simple Gaussian bias correction, while achieving similar overall accuracy.

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

Agromet ◽

10.29244/j.agromet.34.1.20-33 ◽

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.

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Comments on “Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging”

Monthly Weather Review ◽

10.1175/2007mwr1963.1 ◽

2007 ◽

Vol 135 (12) ◽

pp. 4226-4230 ◽

Cited By ~ 33

Author(s):

Thomas M. Hamill

Keyword(s):

Surface Temperature ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System

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

Water ◽

10.3390/w13152124 ◽

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.

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