Journal article
Improved ENSO Forecasting Using Bayesian Updating and the North American Multimodel Ensemble (NMME)
Journal of climate, Vol.30(22), pp.9007-9025
11/15/2017
DOI: 10.1175/JCLI-D-17-0073.1
Abstract
Abstract This study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Niño-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short lead months. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Niño-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Niño-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1–12; particularly for short leads) and target months (from January to December). However, for Niño-3, the BU-Model does not outperform NMME-EM forecasts for leads 7–11 and target months from June to October in terms of correlation and RMSE. Last, the authors test further potential improvements by preselecting “good” models (BU-Model-0.3) and by using principal component analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Niño-3/-3.4 for the 2015/16 El Niño event.
Details
- Title: Subtitle
- Improved ENSO Forecasting Using Bayesian Updating and the North American Multimodel Ensemble (NMME)
- Creators
- Wei Zhang - University of IowaGabriele Villarini - University of IowaLouise Slater - University of IowaGabriel A Vecchi - Princeton UniversityA. Allen Bradley - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of climate, Vol.30(22), pp.9007-9025
- DOI
- 10.1175/JCLI-D-17-0073.1
- ISSN
- 0894-8755
- eISSN
- 1520-0442
- Grant note
- DOI: 10.13039/100007298, name: Climate Program Office, award: NA15OAR4310073; DOI: 10.13039/100007298, name: Climate Program Office, award: NA14OAR4830101; DOI: 10.13039/100000001, name: National Science Foundation, award: AGS-1349827; name: Broad Agency Announcement Program and the Engineer Research and Development Center–Cold Regions Research and Engineering Laboratory under Contract, award: W913E5-16-C-0002
- Language
- English
- Date published
- 11/15/2017
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984197315702771
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