topaz evaluation l. bertino, f. counillon, p. sakov mohn-sverdrup center/nersc godae workshop,...
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TOPAZ evaluation
L. Bertino, F. Counillon, P. Sakov Mohn-Sverdrup Center/NERSC
GODAE workshop, Toulouse, June 2009
TOPAZ System overview
System descriptionValidation of TOPAZ
Data Assimilation
Uncertainty estimates
Hindcast studies
Atmospheric Data
Satellite DataSLA, SST, Ice,
In Situ Data
Analyze the ocean circulation, sea-ice and biogeochemistry. Provide real-time forecasts to the general public and industrial users
EnKFData assimilation
system
User-targeted ocean forecasting
Ocean Primary production
Gulf of Mexicomodel
Atlantic and Arcticmodel
Sea-Icemodel
Eco-system model
The TOPAZ model system TOPAZ3: Atlantic and Arctic
HYCOM + EVP sea-ice model 11- 16 km horizontal resolution 22 hybrid layers
EnKF 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 http://thredds.met.no (MERSEA…)
EnKF Correlations
3rd Jan 2006 8th Nov 2006
The HYCOM model 3D 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
TOPAZ3 TOPAZ4
MICOM
BCM
TOPAZ System overview
System descriptionValidation
Data Assimilation
3 Validation criteriacf 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 4D data
Large data transfers
Solutions adopted (during Mersea Strand 1, 2003-2004) 4 Classes of output products (3D, 2D, time series, residuals) Common output grids (1/8th deg, projection...) Self-documented file format (NetCDF) Inter-operable file access (OPeNDAP/THREDDS)
Arctic Metrics
Validation against hydrographic data
Topaz2 Topaz3 IMR
June07
Sept07
Online comparison to Argo profiles
Sparse profiles under iceNPEO deployment 2006
--- TOPAZ
— NPEO
*: North Pole Environment Observatory
Water fluxes
Sea-ice edgeVisual 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 small scales Fast ice MIZ
Forecast skills by region
Alaska
Barents Sea
Bering Strait
Central Arctic
Greenland Sea Kara Sea
SLA assimilation residualsAzores box
MERSEA sections updated
Blue: MERSEA Class2 sections
Red: Sections from IMR
TOPAZ System overview
System descriptionValidation
Data Assimilation
Assimilation of Ocean Color in Assimilation of Ocean Color in HYCOM-NORWECOMHYCOM-NORWECOM
Data: Satellite Ocean Color (SeaWIFS) Coupled Model: HYCOM-NORWECOM
(7 compartments)Problems:
• Coupled 3-dimensional physical-biological model.• High-dimension.• Non-Gaussian variables.
Perspectives:• Environment monitoring.• Fisheries.• Methodological developmentsfor future coastal HR systems.
Gaussian anamorphosis with the Gaussian anamorphosis with the EnKFEnKF
Simon & Bertino (OSD, 2009)
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
EnKFCut-off of neg. values
Gaussian AnamorphosisEnKF
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