Download
ensemble data assimilation experiments for the coastal ocean impact of different observed variables n.
Skip this Video
Loading SlideShow in 5 Seconds..
Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables PowerPoint Presentation
Download Presentation
Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables

Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables

626 Views Download Presentation
Download Presentation

Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. An ensemble Kalman filter approach to data assimilation for the NY Harbor. Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables Ross N Hoffman1, Rui M Ponte1, Eric Kostelich2, Alan Blumberg3, Istvan Szunyogh4, and Sergey V Vinogradov1 1Atmospheric and Environmental Research, Inc. 2Arizona State University 3Stevens Institute of Technology 4University of Maryland IGARSS 2008 (Boston) FR3.111.4, Friday, 11 July 2008, 14:20

  2. Estuarine and Coastal Ocean Model ECOM • Based on Princeton Ocean Model – POM • 3d, sigma coordinate, curvilinear, C grid • Currents, temperature, salinity, water level • Turbulence energy, length scale • Mellor-Yamada, level 2.5 • High-resolution model grid, allows 50m resolution in rivers • Real-time application • Realistic inter-tidal zone • Comprehensive catalogue of fresh water and thermal sources: 241 treatment plants, 39 power plants, 91 river systems IGARSS (Boston)

  3. LETKF – Local Ensemble Transform Kalman Filter • Kalman filter :: minimizes data misfit and propagate uncertainty consistent with model dynamics and prior information • Ensemble :: error covariance from N forecasts • Local :: each grid point analyzed locally • Transform :: minimize cost function in space spanned by the forecast ensemble • LETKF is efficient and effective • No change required to ocean model in these experiments – no adjoint needed • Used quasi-operationally with NOAA and NASA atmospheric models IGARSS (Boston)

  4. Ensemble data assimilation approach • The ensemble mean is our best estimate; the ensemble spread captures uncertainty • 16 sets of ECOM initial conditions are established by sampling a validated model simulation (nature) • 16 3hr ECOM forecasts made • Nature + errors gives observations • 10% of grid points for each variable are observed • Errors standard deviations: 10 cm, 0.5ºC, 5 cm/s, 1 psu • LETKF optimally combines forecasts and observations • For comparison, a free running forecast from mean IC uses no observations.

  5. Nature run (“True” SST evolution) SST 06 UTC 27 April 2004 SST 16 UTC 28 April 2004 NYC LI NJ • Large change in plume of fresh/warm water over 34 h • Dynamically challenging test case IGARSS (Boston)

  6. Time-height cross sections ECOM/LETKF Analysis Free Running Forecast Location Truth (Nature Run) T (degC) S (psu) Bathymetry Map IGARSS (Boston)

  7. Evolution of T and h Error FRF Analysis IGARSS (Boston)

  8. Surface Salinity Analysis Error Analysis FRF • Map view of SSS error • Analysis errors much smaller than FRF errors • S.D. of error for hours 48-96 • Grid point view of SSS error • Shows rivers and inner harbor IGARSS (Boston)

  9. Findings • Most useful for variables with slower times scales • T, S are slow; u, v, h are fast and adjust quickly to tide and wind forcing so there is little room for improvement • Errors and biases :: greatly reduced by the assimilation • Sensitivity experiments • Works well at all data densities examined • As data density increases, the ensemble spread, bias, and error standard deviation decrease • As ensemble size increases, the ensemble spread increases and error standard deviation decreases • Increases in the size of the observation error lead to a larger ensemble spread but have a small impact on the analysis accuracy IGARSS (Boston)

  10. Data type impact experiments IGARSS (Boston)

  11. Simulated observing network Ferry SST CODAR Buoy IGARSS (Boston)

  12. Layer 1 temperature spread trend oC/hr Filter divergence is only in unobserved river head waters. These areas eliminated in following statistics. IGARSS (Boston)

  13. Naive vs tuned localization T Bias Naive Tuning eliminates filter divergence Tuning improves errors Time T Error T Spread Tuned Tuning very quickly removes bias

  14. Forecast Obs. O-F Sandy Hook, NJ Pier 40, NY Newark, NJ Future work • Real data… • Quality control • Forecast uncertainty provides “ruler” for O-B (obs-bkgrd) • Verification of forecasts and probability forecasts • Model and data bias estimation IGARSS (Boston)

  15. Extensions • Retrieval, ambiguity removal, data analysis at once • ECOM modules include waves, biology, intertidal zone, sediment transport, chemistry transport • LETKF allows general nonlinear obs operators, bias correction for model and observations • Improved ocean forecasting (h,T,u,v,S) will improve forecasting of all other properties and vice versa • Ocean color, turbidity, wave statistics • Not wave observations; maybe wave statistics • Brightness temperatures (SST info) • CODAR line of sight currents • Acoustic data (travel time) • Drifters/gliders (trajectories; positions) • SAR, scatterometer • Targeted observations IGARSS (Boston)

  16. Conclusions • ECOM/NYHOPS is near real-time, and has observation data base + verification tools • LETKF is fully 4-d, efficient (mpi), req. no adjoints • Experiments show LETKF is most useful for T, S • u, v, h adjust quickly to tide and wind forcing so there is little room for improvement • We see only weak coupling between T and S analyses • More realistic simulation experiments indicate tuning of localization is important • Many interesting extensions need exploring • Complex obs operators accommodate unusual data, targeted observations, bias correction IGARSS (Boston)

  17. End • Contact: rhoffman@, www.aer.com • References: • A. F. Blumberg, L. A. Khan, and J. P. St. John, “Threedimensional hydrodynamic simulations of the New York Harbor, Long Island Sound and the New York Bight,” J. Hydrologic Eng., vol. 125, pp. 799–816, 1999. • I. Szunyogh, E. J. Kostelich, G. Gyarmati, E. Kalnay, B R. Hunt, E. Ott, E. Satterfield, and J. A. Yorke, “A local ensemble transform Kalman filter data assimilation system for the NCEP global model,” Tellus A, vol. 60, pp. 113–130, 2008. • R. N. Hoffman, R. M. Ponte, E. J. Kostelich, A. Blumberg, I. Szunyogh, S. V. Vinogradov, and J. M. Henderson, “A simulation study using a local ensemble transform Kalman filter for data assimilation in New York Harbor,” J. Atmos. Oceanic Technol., 2008, In press. IGARSS (Boston)