Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME)
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 (i...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Published: |
American Meteorological Society
2017
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| _version_ | 1797101839425994752 |
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| author | Zhang, W Villarini, G Slater, L Vecchi, G Bradley, A |
| author_facet | Zhang, W Villarini, G Slater, L Vecchi, G Bradley, A |
| author_sort | Zhang, W |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T05:57:36Z |
| format | Journal article |
| id | oxford-uuid:eb0f7ca7-019b-46be-b335-a69e7aa167db |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:57:36Z |
| publishDate | 2017 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | oxford-uuid:eb0f7ca7-019b-46be-b335-a69e7aa167db2022-03-27T11:06:50ZImproved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME)Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:eb0f7ca7-019b-46be-b335-a69e7aa167dbSymplectic Elements at OxfordAmerican Meteorological Society2017Zhang, WVillarini, GSlater, LVecchi, GBradley, AThis 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. |
| spellingShingle | Zhang, W Villarini, G Slater, L Vecchi, G Bradley, A Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title | Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title_full | Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title_fullStr | Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title_full_unstemmed | Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title_short | Improved ENSO forecasting using Bayesian updating and the North American Multimodel Ensemble (NMME) |
| title_sort | improved enso forecasting using bayesian updating and the north american multimodel ensemble nmme |
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