1 / 17

190 likes | 847 Vues

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 Hoffman 1 , Rui M Ponte 1 , Eric Kostelich 2 , Alan Blumberg 3 , Istvan Szunyogh 4 ,

Télécharger la présentation
## Ensemble data assimilation experiments for the coastal ocean: Impact of different observed variables

**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

**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**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)**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)**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.**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)**Time-height cross sections**ECOM/LETKF Analysis Free Running Forecast Location Truth (Nature Run) T (degC) S (psu) Bathymetry Map IGARSS (Boston)**Evolution of T and h Error**FRF Analysis IGARSS (Boston)**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)**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)**Data type impact experiments**IGARSS (Boston)**Simulated observing network**Ferry SST CODAR Buoy IGARSS (Boston)**Layer 1 temperature spread trend**oC/hr Filter divergence is only in unobserved river head waters. These areas eliminated in following statistics. IGARSS (Boston)**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**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)**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)**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)**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)

More Related