1 / 32

A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller

A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller Bruce Cornuelle Scripps Institution of Oceanography, UCSD. Forecast the mesoscale eddies Understand the physics that control their generation and evolution Assess the biological response.

fuller
Download Presentation

A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller

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. A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller Bruce Cornuelle Scripps Institution of Oceanography, UCSD

  2. Forecast the mesoscale eddies Understand the physics that control their generation and evolution Assess the biological response

  3. Observational Dataset California Cooperative Oceanic Fisheries Investigation Hydrography Southern California Coast and Baja Temperature, Salinity and Zooplankton 1949 – 2003 seasonal data 20 m vertical resolution, from 0– 500 m 70 - 80 km horizontal grid CalCOFI historical sampling grid

  4. The Strategy Initialize the model by assimilating the slowly evolving component of the eddy field which can potentially lead to forecast skill over a period of 2 months

  5. The Strategy Initialize the model by assimilating the slowly evolving component of the eddy field which can potentially lead to forecast skill over a period of 2 months The Method The Green’s Function Method

  6. A typical sampling of a mesoscale eddy E1 “SSH”

  7. Assimilation of CalCOFI T,S CalCOFI Coastal observation Ocean Circulation Model Satellite Data AVVISO TOPEX/ERS SSH SSH E2 E2 Data Assimilation Independent verification [m] E1 E1 E1 “SSH” 65% reduction in error variance relative to the model initial guess!

  8. Forecasting and Hindcasting Ocean Productivity Ecosystem Model SeaWIFS CalCOFI in Situ [M N/m3] [M N/m3] E2 E2 Chl-a Chl-a E3 Independent verification E3 E3 E1 E1 Chl-a REFERENCE: Di Lorenzo, E., A. J. Miller, D. J. Neilson, B. D. Cornuelle, and J. R. Moisan, 2003: Modeling observed California Current mesoscale eddies and the ecosystem response. International Journal of Remote Sensing, in press.

  9. You need to know the physics that goes into the assimilation scheme

  10. E1 “SSH”

  11. 60 60 60 1 0 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 SSH Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT SKILL associated with Persistence of Initial Condition

  12. E1 “SSH” Data is collected over a 20 day period

  13. 60 60 60 1 0 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 SKILL evolution when the true initial condition is replaced with a 20 day average in a dynamical forecast SSH Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT

  14. 60 60 60 60 60 60 1 0 50 50 50 50 50 50 40 40 40 40 40 40 30 30 30 30 30 30 20 20 20 20 20 20 10 10 10 10 10 10 SSH Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT days JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT

  15. How about the uncertainties in Forcing Functions?

  16. 60 60 1 0 50 50 40 40 30 30 20 20 10 10 SKILL evolution with errors in Forcing Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT

  17. 60 60 60 60 1 0 50 50 50 50 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 SKILL evolution with errors in Forcing (and Open BC) Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT days JAN APR JUL OCT JAN APR JUL OCT JUNE SKILL evolution with errors in initial condition

  18. Wavenumber Spectra

  19. Concluding remarks: Forecast the mesoscale eddies Real time forecast of CalCOFI in April 2003 SCCOOS nowcast-forecast with UCLA and JPL Understand the physics that control their generation and evolution Error Covariances Seasonal dependence Assess the biological response In progress….

  20. April July (a) (b) Ekman Pumping Δρincrease P. Conception Δρ Wind currents Shelf Δh westward propagation off the Bight (c) Instability processes on continental slope

  21. Ocean Temperature Anomalies 1 C 0 -1 Zooplankton Loge Tot. Vol. 7 6 5 4 1950 1960 1970 1980 1990 2000 Observations along the California Coast

  22. Assimilation Method

  23. Assimilation of CalCOFI T,S CalCOFI Coastal observation E1 “SSH”

  24. Assimilation of CalCOFI T,S CalCOFI Coastal observation Ocean Circulation Model SSH E2 Data Assimilation E1 E1 “SSH”

  25. What have we learned about the mesoscale dynamics?

  26. What have we learned about the mesoscale dynamics?

  27. The Green’s Function Method Green’s Function

  28. 60 60 60 60 1 0 50 50 50 50 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 Changes in SKILL associated with errors in Forcing Surface T T 150 m days 0.5 JAN APR JUL OCT JAN APR JUL OCT days JAN APR JUL OCT JAN APR JUL OCT

More Related