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The future of Earth system prediction: Advances in model-data fusion

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized...

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Autores principales: Gettelman, Andrew, Geer, Alan J., Forbes, Richard M., Carmichael, Greg R., Feingold, Graham, Posselt, Derek J., Stephens, Graeme L., van den Heever, Susan C., Varble, Adam C., Zuidema, Paquita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8985915/
https://www.ncbi.nlm.nih.gov/pubmed/35385304
http://dx.doi.org/10.1126/sciadv.abn3488
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author Gettelman, Andrew
Geer, Alan J.
Forbes, Richard M.
Carmichael, Greg R.
Feingold, Graham
Posselt, Derek J.
Stephens, Graeme L.
van den Heever, Susan C.
Varble, Adam C.
Zuidema, Paquita
author_facet Gettelman, Andrew
Geer, Alan J.
Forbes, Richard M.
Carmichael, Greg R.
Feingold, Graham
Posselt, Derek J.
Stephens, Graeme L.
van den Heever, Susan C.
Varble, Adam C.
Zuidema, Paquita
author_sort Gettelman, Andrew
collection PubMed
description Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.
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spelling pubmed-89859152022-04-19 The future of Earth system prediction: Advances in model-data fusion Gettelman, Andrew Geer, Alan J. Forbes, Richard M. Carmichael, Greg R. Feingold, Graham Posselt, Derek J. Stephens, Graeme L. van den Heever, Susan C. Varble, Adam C. Zuidema, Paquita Sci Adv Earth, Environmental, Ecological, and Space Sciences Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society. American Association for the Advancement of Science 2022-04-06 /pmc/articles/PMC8985915/ /pubmed/35385304 http://dx.doi.org/10.1126/sciadv.abn3488 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Earth, Environmental, Ecological, and Space Sciences
Gettelman, Andrew
Geer, Alan J.
Forbes, Richard M.
Carmichael, Greg R.
Feingold, Graham
Posselt, Derek J.
Stephens, Graeme L.
van den Heever, Susan C.
Varble, Adam C.
Zuidema, Paquita
The future of Earth system prediction: Advances in model-data fusion
title The future of Earth system prediction: Advances in model-data fusion
title_full The future of Earth system prediction: Advances in model-data fusion
title_fullStr The future of Earth system prediction: Advances in model-data fusion
title_full_unstemmed The future of Earth system prediction: Advances in model-data fusion
title_short The future of Earth system prediction: Advances in model-data fusion
title_sort future of earth system prediction: advances in model-data fusion
topic Earth, Environmental, Ecological, and Space Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8985915/
https://www.ncbi.nlm.nih.gov/pubmed/35385304
http://dx.doi.org/10.1126/sciadv.abn3488
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