Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction.

S2S prediction coupled data assimilation data assimilation decadal prediction ocean data assimilation ocean observation network ocean reanalysis reanalysis

Journal

Frontiers in Marine Science
ISSN: 2296-7745
Titre abrégé: Front Mar Sci
Pays: Switzerland
ID NLM: 101636280

Informations de publication

Date de publication:
Jul 2019
Historique:
entrez: 20 9 2019
pubmed: 20 9 2019
medline: 20 9 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31534949
doi: 10.3389/fmars.2019.00391
pmc: PMC6750049
mid: NIHMS1538696
doi:

Types de publication

Journal Article

Langues

eng

Pagination

391

Subventions

Organisme : Goddard Space Flight Center NASA
Pays : United States
Organisme : Intramural NASA
ID : N-999999
Pays : United States

Déclaration de conflit d'intérêts

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Stephen G Penny (SG)

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States.

Santha Akella (S)

National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, United States.

Magdalena A Balmaseda (MA)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Philip Browne (P)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

James A Carton (JA)

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States.

Matthieu Chevallier (M)

Météo-France, Toulouse, France.

Francois Counillon (F)

Nansen Environmental and Remote Sensing Center, Bergen, Norway.

Catia Domingues (C)

Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, TAS, Australia.

Sergey Frolov (S)

Naval Research Laboratory, Monterey, CA, United States.

Patrick Heimbach (P)

The University of Texas at Austin, Austin, TX, United States.

Patrick Hogan (P)

Naval Research Laboratory, Stennis Space Center, MS, United States.

Ibrahim Hoteit (I)

King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Doroteaciro Iovino (D)

Euro-Mediterranean Center on Climate Change, Lecce, Italy.

Patrick Laloyaux (P)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Matthew J Martin (MJ)

Met Office, Exeter, United Kingdom.

Simona Masina (S)

Euro-Mediterranean Center on Climate Change, Lecce, Italy.

Andrew M Moore (AM)

University of California, Santa Cruz, Santa Cruz, CA, United States.

Patricia de Rosnay (P)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Dinand Schepers (D)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Bernadette M Sloyan (BM)

Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia.

Andrea Storto (A)

NATO Centre for Maritime Research and Experimentation, La Spezia, Italy.

Aneesh Subramanian (A)

Department of Atmospheric and Oceanic Science, University of Colorado, Boulder, Boulder, CO, United States.

SungHyun Nam (S)

Seoul National University, Seoul, South Korea.

Frederic Vitart (F)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Chunxue Yang (C)

Istituto di Scienze Marine, Consiglio Nazionale delle Ricerche, Rome, Italy.

Yosuke Fujii (Y)

JMA Meteorological Research Institute, Tsukuba, Japan.

Hao Zuo (H)

European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom.

Terry O'Kane (T)

Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia.

Paul Sandery (P)

Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia.

Thomas Moore (T)

Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia.

Christopher C Chapman (CC)

Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia.

Classifications MeSH