1 / 13

Outline Poseidon OGCM and GMAO coupled forecasting system

tale
Download Presentation

Outline Poseidon OGCM and GMAO coupled forecasting system

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ensemble Data Assimilation and its Impacton Seasonal Hindcast SkillChristian Keppenne1, Michele Rienecker2, Nicole Kurkowski3, Jossy Jacob4 & Robin Kovach51,3,4,5Science Applications International Corporation San Diego, CA 921212Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbelt, MD 20771AGU 2006 Spring Meeting, Baltimore, MD1clk@gmao.gsfc.nasa.gov, 2michele.rienecker@nasa.gov, 3kurkowsk@gmao.gsfc.nasa.gov , 4jjacob@gmao.gsfc.nasa.gov , 5kovach@gmao.gsfc.nasa.gov

  2. Outline • Poseidon OGCM and GMAO coupled forecasting system • Ensemble Kalman filter (EnKF) implementation for Poseidon • Impact of T+SSH assimilation on CGCM seasonal hindcast skill

  3. GMAO CGCM coupled forecasting system • - GMAO AGCM, 2 x 2.5 x L34 • - LSM: Mosaic (SVAT) • OGCM: Poseidon v4, 1/3 x 5/8 x L27 • Production ODAS: OI of in situ temperature profiles with salinity adjustment • - Experimental ODAS: EnKF • Note: ODAS is run on OGCM prior to coupling to AGCM & LSM OGCM • 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 CGCM and OGCM

  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 • System-noise modelling • Model errors: use model EOFs to generate state perturbations • Forcing errors: add random perturbations to wind stress forcing Online bias estimation • Used in SSH assimilation Hybrid (3DVAR + ensemble) covariances • Used in T assimilation Spatio-temporal filtering of covariances • To mitigate effect of small ensemble sizes Computational cost • About 4 hours/month on 240 SGI Altix CPUs for 16 members EnKF implementation

  5. a) Standard assimilation true climate Assimilation alters the model climatology observable model climate b) Assimilation with Online bias estimation (OBE) true climate est. bias Estimates of variability (biased model state) and climatological error (bias) are evolved side by side observable model climate Online bias estimation

  6. EnKF-8: No filter, H-section EnKF-8: filter, H-section EnKF-8: filter, V-section EnKF-8: No filter, V-section EnKF-32: No filter, H-section EnKF-32: No filter, V-section • Temporal filter (exponential moving average) to reduce weather noise • Spatial filter to lessen effect of running small ensembles Covariance filtering

  7. Assimilation experiment • Assimilate T/P anomalies + TAO & XBT temperature profiles 1/1/2001-12/31/2001 • Online bias estimation in SSH assimilation • Random forcing from 32 OGCM leading EOFs + wind-stress perturbations • Compare 8, 16 & 32 member EnKF • Compare with • no-assimilation control • Production ODAS (temperature OI + salinity correction) Experiments

  8. SSH OMF and OMA statistics Note the differences in scales! mean OMF RMS OMF mean OMA RMS OMA T OMF and OMA statistics (Hybrid covariances used in T assimilation with EnKF result in stronger data weights than OI+S(T)) EnKF8 Control OI+S(T) EnKF8 EnKF16 EnKF32 Control EnKF16 OI+S(T) • Control is most biased • OI partly corrects SSH bias • EnKF runs have no noticeable SSH bias EnKF32

  9. Impact of assimilation on CGCM hindcast skill • 16-member EnKF • Assimilate T + SSH for February-April of each year of 1993-2002 • Couple OGCM to AGCM & LSM after running ODAS • 12-month CGCM hindcasts initialized using ocean states from EnKF runs (to save CPU time, CGCM hindcasts have only 5 ensemble member) • Assess impact of assimilation on SST & SSH hindcast skill • Compare to history of CGCMv1 May-start hindcasts (problematic month)

  10. 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

  11. EnKF T+SSH, OBE EN underestimated NSIPP CGCMv1 tier 1 False EN alert missed EN missed EN missed EN false LN alert false LN alert Niño-3 SST

  12. 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

  13. Conclusions • Online bias correction important when T data & SSH anomalies are assimilated. • Filtering of covariances effective in lessening the impact of running a low-rank EnKF • EnKF-initialized hindcasts can miss or underestimate El Niño events but result in less false El Niño or La Niña alerts than production forecasts

More Related