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Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction
Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6750049/ https://www.ncbi.nlm.nih.gov/pubmed/31534949 http://dx.doi.org/10.3389/fmars.2019.00391 |
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author | Penny, Stephen G. Akella, Santha Balmaseda, Magdalena A. Browne, Philip Carton, James A. Chevallier, Matthieu Counillon, Francois Domingues, Catia Frolov, Sergey Heimbach, Patrick Hogan, Patrick Hoteit, Ibrahim Iovino, Doroteaciro Laloyaux, Patrick Martin, Matthew J. Masina, Simona Moore, Andrew M. de Rosnay, Patricia Schepers, Dinand Sloyan, Bernadette M. Storto, Andrea Subramanian, Aneesh Nam, SungHyun Vitart, Frederic Yang, Chunxue Fujii, Yosuke Zuo, Hao O’Kane, Terry Sandery, Paul Moore, Thomas Chapman, Christopher C. |
author_facet | Penny, Stephen G. Akella, Santha Balmaseda, Magdalena A. Browne, Philip Carton, James A. Chevallier, Matthieu Counillon, Francois Domingues, Catia Frolov, Sergey Heimbach, Patrick Hogan, Patrick Hoteit, Ibrahim Iovino, Doroteaciro Laloyaux, Patrick Martin, Matthew J. Masina, Simona Moore, Andrew M. de Rosnay, Patricia Schepers, Dinand Sloyan, Bernadette M. Storto, Andrea Subramanian, Aneesh Nam, SungHyun Vitart, Frederic Yang, Chunxue Fujii, Yosuke Zuo, Hao O’Kane, Terry Sandery, Paul Moore, Thomas Chapman, Christopher C. |
author_sort | Penny, Stephen G. |
collection | PubMed |
description | Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network. |
format | Online Article Text |
id | pubmed-6750049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-67500492020-07-01 Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction Penny, Stephen G. Akella, Santha Balmaseda, Magdalena A. Browne, Philip Carton, James A. Chevallier, Matthieu Counillon, Francois Domingues, Catia Frolov, Sergey Heimbach, Patrick Hogan, Patrick Hoteit, Ibrahim Iovino, Doroteaciro Laloyaux, Patrick Martin, Matthew J. Masina, Simona Moore, Andrew M. de Rosnay, Patricia Schepers, Dinand Sloyan, Bernadette M. Storto, Andrea Subramanian, Aneesh Nam, SungHyun Vitart, Frederic Yang, Chunxue Fujii, Yosuke Zuo, Hao O’Kane, Terry Sandery, Paul Moore, Thomas Chapman, Christopher C. Front Mar Sci Article Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network. 2019-07 /pmc/articles/PMC6750049/ /pubmed/31534949 http://dx.doi.org/10.3389/fmars.2019.00391 Text en http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Article Penny, Stephen G. Akella, Santha Balmaseda, Magdalena A. Browne, Philip Carton, James A. Chevallier, Matthieu Counillon, Francois Domingues, Catia Frolov, Sergey Heimbach, Patrick Hogan, Patrick Hoteit, Ibrahim Iovino, Doroteaciro Laloyaux, Patrick Martin, Matthew J. Masina, Simona Moore, Andrew M. de Rosnay, Patricia Schepers, Dinand Sloyan, Bernadette M. Storto, Andrea Subramanian, Aneesh Nam, SungHyun Vitart, Frederic Yang, Chunxue Fujii, Yosuke Zuo, Hao O’Kane, Terry Sandery, Paul Moore, Thomas Chapman, Christopher C. Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title | Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title_full | Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title_fullStr | Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title_full_unstemmed | Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title_short | Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction |
title_sort | observational needs for improving ocean and coupled reanalysis, s2s prediction, and decadal prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6750049/ https://www.ncbi.nlm.nih.gov/pubmed/31534949 http://dx.doi.org/10.3389/fmars.2019.00391 |
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