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Title: A Hybrid Global Ocean Data Assimilation System at NCEP

Journal Article · · Monthly Weather Review
 [1];  [2];  [3];  [3]
  1. National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). National Centers for Environmental Prediction; Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science
  2. National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). National Centers for Environmental Prediction
  3. Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science
+ Show Author Affiliations

Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. Finally, the Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE Office of Science (SC), Innovative and Novel Computational Impact on Theory and Experiment Program (INCITE)
OSTI ID:
1565521
Journal Information:
Monthly Weather Review, Vol. 143, Issue 11; ISSN 0027-0644
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 62 works
Citation information provided by
Web of Science

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Observing system experiments over the Atlantic Ocean with the REMO ocean data assimilation system (RODAS) into HYCOM journal November 2019
Using the hybrid gain algorithm to sample data assimilation uncertainty
  • Houtekamer, Pieter Leopold; Buehner, Mark; De La Chevrotière, Michèle
  • Quarterly Journal of the Royal Meteorological Society, Vol. 145, Issue S1 https://doi.org/10.1002/qj.3426
journal December 2018
El Niño–Southern Oscillation complexity journal July 2018
Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea journal March 2017
Impact of dynamical representational errors on an Indian Ocean ensemble data assimilation system
  • Sanikommu, Sivareddy; Banerjee, Deep Sankar; Baduru, Balaji
  • Quarterly Journal of the Royal Meteorological Society, Vol. 145, Issue 725 https://doi.org/10.1002/qj.3649
journal October 2019
An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean journal January 2019
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