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