Brassington: merging GHRSST and GODAE for SST forecasti

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Towards merging GHRSST and GODAE
for SST forecasting
Brassington, Pugh, Beggs and Oke
Bureau of Meteorology
CSIRO Marine and Atmospheric Research
www.csiro.au
Outline
Pathways for GHRSST => GODAE
GODAE’s role in SST
Bureau systems
Ocean forecasting for Australia, OceanMAPS
 Version 1.0
 First forecasts
Assimilating SSHA and SST
Pathways for GHRSST => GODAE
GHRSST => GODAE
Validation of GODAE products
GHRSST => NWP => GODAE
Analysed surface fluxes
GHRSST => GODAE <=> NWP
Assimilated SST, improved analysed and forecast currents
Forecasted SST
GHRSST => GODAE <=> WAM <=> NWP
Forecast currents for wave refraction
GHRSST => BRAN <=> GODAE
Reanalysed SST, feedback on GODAE system design
GHRSST => MCC => GODAE
Filling the data gap in altimetry
GODAE’s role in SST
GODAE => GHRSST
Dynamical background analysis
Methodology for SSHA
GODAE offers a forecast capability for SST
NWP relies on a static SST analysis for forecast b.c.’s
Skill threshold some way off for NWP uptake
Improved coupled NWP fluxes benefit back to GODAE
GODAE ideally should be able to match GHRSST analyses
Several impediments
Dynamical interpolation uncertainties
Sources of uncertainty
NWP fluxes
Mixed layer scheme and other paramterisations
Predictability of ocean dynamics
Model resolution
Assimilation method
Specification of covariances cov(SSHA,SST)
…
Pluses
Non gaussian covariances
e-folding scale defined by model variability
Multi-variate covariances
BODAS continues to prove it is a good strategy for ocean forecasting
Bureau systems
Ocean, Analysis and Prediction System (OceanMAPS)
Brassington et al
High Resolution Sea Surface Temperature (HRSST)
Beggs et al
Australian Wave Model (AusWAM)
Greenslade et al
Global atmospheric prediction system (GASP)
Seaman et al
Moving to UKMet Office UM
Coupled limited area model
TC-LAPS<=>AusWAM<=>OceanMAPS
Ocean Model, Analysis and Prediction System
(OceanMAPSv1)
OFAM
MOM4p0d
1/10ºx1/10º (90E-180E, 70S-16N)
10m (0-200m)
BODAS
Multi-variate optimal interpolation (T, S, eta)
Model error covariances => 72 member ensemble of anomalies
+/- 5days altimetry
Localisation 8ºx8º
Background, daily average
Surface fluxes
GASP
Observations
Jason1, Envisat SSHA products
AMSR-E descending track
GTS, GDAC Argo in situ
OceanMAPS schedule
Current status and performance
Case study: monster eddy
Case study: EAC drifter experiment
 EAC drifter experiment
 8 drifters deployed from the PX30 line
SSHA + SST => SST nowcasts/forecasts
BLUElink has demonstrated the advantage of GHRSST products to an
ocean re-analysis
(a) Removes obvious biases
(b) Multi-variate does modify the sub-surface structure (Oke)
(c) Modifies near surface currents, quantitative improvement and
indications of skill over persistence
Positive impact has accelerated implementation into OceanMAPSv1.0
Results have translated to removal of bias
Availability of GHRSST products made this feasible
AMSR-E 25km resolution matches OFAM and coverage
Microwave data gap is a concern
Surface ocean currents
Impact of HRSST for LAPS
Impact of HRSST for LAPS
GHRSST requirements
OceanMAPS
L2P or L3P foundation for direct assimilation
L4 foundation and skin for validation
Error bars - normalised
Documentation
Timeliness (Real-time to 10 days behind)
Resolution (1/16 degree)
Diurnal model (model foundation to skin)
Data gap
BRAN
Reanalysed L2P or L3P
Conclusions
GHRSST-PP is serving the ocean prediction community well
Unlocked the power of the observation to the non-specialist
Plans and funding for continuity of GHRSST products very positive
Thank you
Very rapid implementation of AMSR-E
OceanMAPS and BRAN demonstrating clear improvements
Requirements for GHRSST products will continue to grow
BODAS dynamical based analysis scheme
very positive results
need to be optimised to control uncertainties
provide many advantage
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