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...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2019-07-01
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Series: | Frontiers in Marine Science |
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Online Access: | https://www.frontiersin.org/article/10.3389/fmars.2019.00391/full |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stephen G. Penny Santha Akella Magdalena A. Balmaseda Philip Browne James A. Carton Matthieu Chevallier Francois Counillon Catia Domingues Sergey Frolov Patrick Heimbach Patrick Hogan Ibrahim Hoteit Doroteaciro Iovino Patrick Laloyaux Matthew J. Martin Simona Masina Andrew M. Moore Patricia de Rosnay Dinand Schepers Bernadette M. Sloyan Andrea Storto Aneesh Subramanian SungHyun Nam Frederic Vitart Chunxue Yang Yosuke Fujii Hao Zuo Terry O’Kane Paul Sandery Thomas Moore Christopher C. Chapman |
spellingShingle |
Stephen G. Penny Santha Akella Magdalena A. Balmaseda Philip Browne James A. Carton Matthieu Chevallier Francois Counillon Catia Domingues Sergey Frolov Patrick Heimbach Patrick Hogan Ibrahim Hoteit Doroteaciro Iovino Patrick Laloyaux Matthew J. Martin Simona Masina Andrew M. Moore Patricia de Rosnay Dinand Schepers Bernadette M. Sloyan Andrea Storto Aneesh Subramanian SungHyun Nam Frederic Vitart Chunxue Yang Yosuke Fujii Hao Zuo Terry O’Kane Paul Sandery Thomas Moore Christopher C. Chapman Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction Frontiers in Marine Science data assimilation reanalysis coupled data assimilation S2S prediction decadal prediction ocean observation network |
author_facet |
Stephen G. Penny Santha Akella Magdalena A. Balmaseda Philip Browne James A. Carton Matthieu Chevallier Francois Counillon Catia Domingues Sergey Frolov Patrick Heimbach Patrick Hogan Ibrahim Hoteit Doroteaciro Iovino Patrick Laloyaux Matthew J. Martin Simona Masina Andrew M. Moore Patricia de Rosnay Dinand Schepers Bernadette M. Sloyan Andrea Storto Aneesh Subramanian SungHyun Nam Frederic Vitart Chunxue Yang Yosuke Fujii Hao Zuo Terry O’Kane Paul Sandery Thomas Moore Christopher C. Chapman |
author_sort |
Stephen G. Penny |
title |
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_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_sort |
observational needs for improving ocean and coupled reanalysis, s2s prediction, and decadal prediction |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Marine Science |
issn |
2296-7745 |
publishDate |
2019-07-01 |
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. |
topic |
data assimilation reanalysis coupled data assimilation S2S prediction decadal prediction ocean observation network |
url |
https://www.frontiersin.org/article/10.3389/fmars.2019.00391/full |
work_keys_str_mv |
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doaj-cb34729933f646bdbaeb727665fc81e12020-11-25T01:43:07ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452019-07-01610.3389/fmars.2019.00391434319Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal PredictionStephen G. Penny0Santha Akella1Magdalena A. Balmaseda2Philip Browne3James A. Carton4Matthieu Chevallier5Francois Counillon6Catia Domingues7Sergey Frolov8Patrick Heimbach9Patrick Hogan10Ibrahim Hoteit11Doroteaciro Iovino12Patrick Laloyaux13Matthew J. Martin14Simona Masina15Andrew M. Moore16Patricia de Rosnay17Dinand Schepers18Bernadette M. Sloyan19Andrea Storto20Aneesh Subramanian21SungHyun Nam22Frederic Vitart23Chunxue Yang24Yosuke Fujii25Hao Zuo26Terry O’Kane27Paul Sandery28Thomas Moore29Christopher C. Chapman30Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United StatesNational Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, United StatesEuropean Centre for Medium-Range Weather Forecasts, Reading, United KingdomEuropean Centre for Medium-Range Weather Forecasts, Reading, United KingdomDepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United StatesMétéo-France, Toulouse, FranceNansen Environmental and Remote Sensing Center, Bergen, NorwayAntarctic Climate and Ecosystems Cooperative Research Centre, Hobart, TAS, AustraliaNaval Research Laboratory, Monterey, CA, United StatesThe University of Texas at Austin, Austin, TX, United StatesNaval Research Laboratory, Stennis Space Center, MS, United States0King Abdullah University of Science and Technology, Thuwal, Saudi Arabia1Euro-Mediterranean Center on Climate Change, Lecce, ItalyEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom2Met Office, Exeter, United Kingdom1Euro-Mediterranean Center on Climate Change, Lecce, Italy3University of California, Santa Cruz, Santa Cruz, CA, United StatesEuropean Centre for Medium-Range Weather Forecasts, Reading, United KingdomEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom4Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia5NATO Centre for Maritime Research and Experimentation, La Spezia, Italy6Department of Atmospheric and Oceanic Science, University of Colorado, Boulder, Boulder, CO, United States7Seoul National University, Seoul, South KoreaEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom8Istituto di Scienze Marine, Consiglio Nazionale delle Ricerche, Rome, Italy9JMA Meteorological Research Institute, Tsukuba, JapanEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom4Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia4Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia4Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia4Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, AustraliaDevelopments 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.https://www.frontiersin.org/article/10.3389/fmars.2019.00391/fulldata assimilationreanalysiscoupled data assimilationS2S predictiondecadal predictionocean observation network |