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An ensemble ocean data assimilation system for seasonal prediction

Citation

Yin, Y and Alves, O and Oke, PR, An ensemble ocean data assimilation system for seasonal prediction, Monthly Weather Review, 139, (3) pp. 786-808. ISSN 0027-0644 (2016) [Refereed Article]


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DOI: doi:10.1175/2010MWR3419.1

Abstract

A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts that are forced with perturbed surface fluxes. The small ensemble is augmented with multiple small ensembles from previous assimilation cycles, yielding a larger ensemble that consists of perturbed forecasts from the last month. This larger ensemble is used to represent the system's time-dependent background error covariance. At each assimilation cycle, a central analysis is computed utilizing the ensemble-based covariance. Each of the perturbed ensemble members are nudged toward the central analysis to control the ensemble spread and mean. The ensemble-based covariances generated by PEODAS potentially yield dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for example during El Niño and La Niña conditions. These differences are explored in terms of the dominant dynamics and the system's errors. The performance of PEODAS during a 27-yr reanalysis is evaluated through a series of comparisons with assimilated and independent observations. When compared to its predecessor, POAMA version 1, and a simulation with no assimilation of subsurface observations, PEODAS demonstrates a quantitative improvement in skill. PEODAS will form the basis of Australia's next operational seasonal prediction system.

Item Details

Item Type:Refereed Article
Keywords:data assimilation, ensembles, ENSO, ocean models, seasonal forecasting
Research Division:Earth Sciences
Research Group:Atmospheric Sciences
Research Field:Meteorology
Objective Division:Environment
Objective Group:Atmosphere and Weather
Objective Field:Weather
Author:Oke, PR (Dr Peter Oke)
ID Code:119907
Year Published:2016 (online first 2011)
Web of Science® Times Cited:63
Deposited By:Centre for Oceans and Cryosphere
Deposited On:2017-08-07
Last Modified:2017-09-25
Downloads:6 View Download Statistics

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