Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation
Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2021
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| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1390 |
| الوسوم: |
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| _version_ | 1857415062784835584 |
|---|---|
| author | Tejedor, Alejandro |
| author2 | Stevens, Abby Willett, Rebecca Mamalakis, Antonios Foufoula-Georgiou, Efi Randerson, James T. Smyth, Padhraic Wright, Stephen |
| author2_role | author author author author author author author |
| author_facet | Tejedor, Alejandro Stevens, Abby Willett, Rebecca Mamalakis, Antonios Foufoula-Georgiou, Efi Randerson, James T. Smyth, Padhraic Wright, Stephen |
| author_role | author |
| dc.creator.none.fl_str_mv | Tejedor, Alejandro Stevens, Abby Willett, Rebecca Mamalakis, Antonios Foufoula-Georgiou, Efi Randerson, James T. Smyth, Padhraic Wright, Stephen |
| dc.date.none.fl_str_mv | 2021 2023-03-09T10:58:17Z 2023-03-09T10:58:17Z |
| dc.identifier.none.fl_str_mv | 0894-8755 1520-0442 https://depot.sorbonne.ae/handle/20.500.12458/1390 10.1175/JCLI-D-20-0079.1 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Journal of Climate |
| dc.title.none.fl_str_mv | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space–time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction. |
| id | sorbonner_1f9ac7007dcf8b2d12ddadd145a7f4e8 |
| identifier_str_mv | 0894-8755 1520-0442 10.1175/JCLI-D-20-0079.1 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1390 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter PrecipitationTejedor, AlejandroStevens, AbbyWillett, RebeccaMamalakis, AntoniosFoufoula-Georgiou, EfiRanderson, James T.Smyth, PadhraicWright, StephenUnderstanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space–time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction.2023-03-09T10:58:17Z2023-03-09T10:58:17Z2021Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article0894-87551520-0442https://depot.sorbonne.ae/handle/20.500.12458/139010.1175/JCLI-D-20-0079.1enJournal of Climateoai:depot.sorbonne.ae:20.500.12458/13902023-06-14T09:41:18Z |
| spellingShingle | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation Tejedor, Alejandro |
| title | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| title_full | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| title_fullStr | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| title_full_unstemmed | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| title_short | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| title_sort | Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1390 |