TOPAZ evaluation L Bertino F Counillon P Sakov
TOPAZ evaluation L. Bertino, F. Counillon, P. Sakov Mohn-Sverdrup Center/NERSC GODAE workshop, Toulouse, June 2009
TOPAZ System overview System description Validation of TOPAZ Data Assimilation
Satellite Data SLA, SST, Ice, In Situ Data En. KF Data assimilation system Atmospheric Data Sea-Ice model Atlantic and Arctic model Ecosystem model Uncertainty estimates Ocean Primary production Hindcast studies Gulf of Mexico model User-targeted ocean forecasting Analyze the ocean circulation, sea-ice and biogeochemistry. Provide real-time forecasts to the general public and industrial users
The TOPAZ model system § TOPAZ 3: Atlantic and Arctic § HYCOM + EVP sea-ice model § 11 - 16 km horizontal resolution § 22 hybrid layers § En. KF § 100 members § Observations § Sea Level Anomalies (CLS) § Sea Surface Temperatures (NOAA) § Sea Ice Concentr. (AMSR, NSIDC) § Sea ice drift (CERSAT) § Argo T/S profiles (Coriolis) § Runs weekly, 10 days forecasts § ECMWF forcing § http: //topaz. nersc. no/thredds
En. KF Correlations 3 rd Jan 2006 8 th Nov 2006
The HYCOM model § 3 D numerical ocean model § Hybrid Coordinate Ocean model, HYCOM (U. Miami) § US Navy global forecasts § Hybrid coordinate § Isopycnal in the interior § Z-coordinate at the surface § Terrain following (sigma) § Nesting capability § Coupled § Sea-ice model § Ecosystem models § Large community (http: //www. hycom. org)
Nesting § Bring dynamically consistent information from large-scale circulation to coastal seas § One-way nesting § “Flather” condition for barotropic mode § Avoids reflection of waves at the boundary § Simple relaxation for the baroclinic mode § And for the tracers § Arbitrary resolution and orientation of the nested grids
Effect of the upgrade Weekly SSS in Dec. 1999, free run MICOM BCM TOPAZ 3 TOPAZ 4
TOPAZ System overview System description Validation Data Assimilation
3 Validation criteria cf weather forecasting (Murphy, 93) § Consistency § Are the operational forecasts in agreement with known processes of the ocean circulation? § Accuracy § How close to reality are the results? § Performance (value) § Advantage over any trivial forecast? § climatology, persistence
Validation Metrics § Problems: § Validating and comparing GODAE systems consistently § Different model horizontal grids / Vertical coordinates § Large amounts of 4 D data § Large data transfers § Solutions adopted (during Mersea Strand 1, 2003 -2004) § 4 Classes of output products (3 D, 2 D, time series, residuals) § Common output grids (1/8 th deg, projection. . . ) § Self-documented file format (Net. CDF)
Arctic Metrics
Validation against hydrographic data June 07 Sept 07 Topaz 2 Topaz 3 IMR
Online comparison to Argo profiles
Sparse profiles under ice NPEO deployment 2006 --- TOPAZ — NPEO *: North Pole Environment Observatory
Water fluxes
Sea-ice edge Visual comparison § § § Ice concentration from model in color, SSMI 15% ice contour in black. Ice drift is overlaid. Good overall correspondence between model and data Visual comparison allows identification of problematic regions § West of Novaya Zemlya - a tendency for the ice edge to drift too little to the north during a forecast cycle § South of Svalbard (Bear Island) model ice edge too far to the north § Issues related to model physics § Ice-ocean momentum exchange § Ice models neglect physics which may be important on
Forecast skills by region Barents Sea Alaska Bering Strait Central Arctic Greenland Sea Kara Sea
SLA assimilation residuals Azores box
MERSEA sections updated § Blue: MERSEA Class 2 sections § Red: Sections from IMR
TOPAZ System overview System description Validation Data Assimilation
Assimilation of Ocean Color in HYCOM-NORWECOM Data: Satellite Ocean Color (Sea. WIFS) Coupled Model: HYCOM-NORWECOM (7 compartments) Problems: • Coupled 3 -dimensional physical-biological model. • High-dimension. • Non-Gaussian variables. Perspectives: • Environment monitoring. • Fisheries. • Methodological developments for future coastal HR systems.
Gaussian anamorphosis with the En. KF Anamorphosis: prior transformation of the variables in a Gaussian space (Bertino et al. 2003) Twin experiments (surface chlorophyll-a synthetic observations) Surface CHLa RMS error En. KF Cut-off of neg. values Gaussian Anamorphosis En. KF Simon & Bertino (OSD, 2009)
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