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Outline Ensemble Kalman filter (EnKF) implementation for Poseidon OGCM

norman-riggs
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Outline Ensemble Kalman filter (EnKF) implementation for Poseidon OGCM

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  1. Online Bias correction and Altimetry Assimilation into a High Resolution OGCM with an Ensemble Kalman Filter and Impact on Seasonal Forecasts Christian L. Keppenne1, Michele M. Rienecker2, Nicole P. Kurkowski3 and David D. Adamec51,3Science Applications International Corporation San Diego, CA 921212,4Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbelt, MD 20771JCSDA Science MeetingApril 20-21, 20051keppenne@gsfc.nasa.gov, 2rienecker@gsfc.nasa.gov, 3kurkowski@gsfc.nasa.gov , 4adamec@gsfc.nasa.gov

  2. Outline • Ensemble Kalman filter (EnKF) implementation for Poseidon OGCM • Assimilation experiments with online bias estimation • Impact of initializating OGCM with EnKF on CGCM seasonal hindcast skill

  3. OGCM (ocean component of GMAO CGCMv1 forecasting system) • Poseidon (Schopf and Loughe, 1995) • Quasi-isopycnal vertical coordinate • Prognostic variables are h, t, s, u and v • Sea surface height (SSH) is diagnostic: h = Sibuoyancy(ti, si) hi • About 30 million prognostic variables at 1/3 x 5/8 x L27 resolution S/I Forecasting Objectives • Replace Temperature OI with Temperature + SSH EnKF in ODAS • Initialization of ocean component of CGCM ensembles with EnKF

  4. Multivariate update • Multivariate compactly supported covariances • Update T, S, u & v • Layer thicknesses (h) adjust between analyses • Incremental update • Process SSH and T observations separately Massively parallel implementation (Keppenne and Rienecker, MWR 128, 1971-1981) (Covariance scales determine size of PE regions and # of local observations) • 4 hours/month on 256 PEs for 16 members on current platform: Compaq SC 45 Online bias estimation • Used in SSH assimilation EnKF implementation

  5. Monthly mean TAO temperature 01/1998 Equatorial Pacific too cold near surface and below thermocline in free-model run T OI (without S(T) correction) improves T field over free- model run but S is degraded T+SSH EnKF also improves T field and preserves salinity T+SSH EnKF vs. T OI

  6. Online bias estimation in SSH assimilation Problem • T/P along-track data are anomalies • Model climatology changes as data are assimilated • Introduction of systematic errors (particularly severe in eastern Pacific) Solution • Assimilate unbiased innovations: (z - (wf - b)) • Update bias estimate • Update model • Run bias estimate and ocean state side by side observations forecast bias estimate

  7. a) Standard assimilation true climate observable model climate b) Assimilation with Online bias estimation (OBE) true climate est. bias observable model climate Model is pulled away from model climatology towards observed climate Estimates of variability (biased model state) and climatological error (bias) are evolved in parallel

  8. a) Standard update equation b) Online bias estimation • Pwand Pb are estimated from same ensemble distribution • Covariances are localized but Pb is allowed to have larger scales than Pw

  9. Assimilation experiment • 16-member EnKF • Assimilate T/P anomalies + TAO & XBT temperature profiles 1/1/93-12/31/93 • Online bias estimation in SSH assimilation • Compare with no-assimilation control and with EnKF run without OBE

  10. Marginal change to the SSH bias estimate in response to the same 0.1m SSH innovation Marginal change to depth of 20C isotherm in response to a 0.1m SSH innovation at (0N, 100W) on two different dates

  11. Root mean square OMF differences for T & SSH SSH T • RMS T OMF is 34% below control and RMS SSH OMF is 7% below control in EnKF run without bias estimation in SSH assimilation • RMS OMF are 39% lower than control for T and 23% lower for SSH in run with bias estimation in SSH assimilation

  12. Estimated SSH bias after 6 months Estimated SSH bias after 12 months

  13. Impact of assimilation on CGCM hindcast skill • Assimilate T + SSH for February-April of each year since 1993 • 16-member ensembles • Start 12-month hindcasts using ocean states at the end of assimilation runs • Assess impact of assimilation on SST and SSH hindcast skill • Compare to history of CGCMv1 May-start hindcasts (to save CPU time, only 5 EnKF ensemble members are used to initialize hindcasts)

  14. EnKF T+SSH, OBE NSIPP CGCMv1 tier 1 False EN alert missed EN missed EN missed EN false LN alert false LN alert Niño-3.4 SST

  15. Niño-3 SSH EnKF T+SSH, OBE GMAO CGCMv1 Niño-3.4 SSH Although minor 1995 warm event is missed and 1998 EN is underestimated, the correlation with TOPEX is generally higher in the hindcasts initialized with EnKF T+SSH SSH hindcasts

  16. Conclusions • Although only 16 members are used, use of EnKF has positive impact on seasonal hindcast skill • Use of online bias estimation in SSH assim. markedly improves the correlation with TOPEX of model SSH hindcasts • EnKF-initialized hindcasts produce less false El Niño or La Niña alerts than the CGCMv1 hindcasts initialized with temperature OI • Future: increase ensemble size on NAS Columbia system

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