1 / 17

Towards merging GHRSST and GODAE for SST forecasting

Towards merging GHRSST and GODAE for SST forecasting. Brassington, Pugh, Beggs and Oke Bureau of Meteorology CSIRO Marine and Atmospheric Research. Outline. Pathways for GHRSST => GODAE GODAE’s role in SST Bureau systems Ocean forecasting for Australia, OceanMAPS Version 1.0

brosh
Download Presentation

Towards merging GHRSST and GODAE for SST forecasting

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. Towards merging GHRSST and GODAE for SST forecasting Brassington, Pugh, Beggs and Oke Bureau of Meteorology CSIRO Marine and Atmospheric Research

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. OceanMAPS schedule

  9. Current status and performance

  10. Case study: monster eddy

  11. Case study: EAC drifter experiment • EAC drifter experiment • 8 drifters deployed from the PX30 line

  12. 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

  13. Surface ocean currents

  14. Impact of HRSST for LAPS

  15. Impact of HRSST for LAPS

  16. 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

  17. 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

More Related