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

1
Ensemble data assimilation experiments for the
coastal ocean Impact of different observed
variables
An ensemble Kalman filter approach to data
assimilation for the NY Harbor.

• 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, 1420

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

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

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

6
Time-height cross sections
ECOM/LETKF Analysis
Free Running Forecast
Location
Truth (Nature Run)
T (degC)
S (psu)
Bathymetry Map
7
Evolution of T and h Error
FRF
Analysis
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

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

10
Data type impact experiments
11
Temp. 10 km S of LI

• Nature
• All
• H
• T
• S
• FRF

12
Salinity _at_ GW Bridge

• Nature
• All
• H
• T
• S
• FRF

13
Observations in random columns
Baseline
Mobile
Fixed
Slow decrease In errors
T Error
Time
Filter appears to be diverging
T Spread
14
Simulated observing network
Ferry
SST
CODAR
Buoy
15
Layer 1 temperature spread trend
oC/hr
Filter divergence is only in unobserved river
head waters. These areas eliminated in following
statistics.
16
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
17
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

18
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

19
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

20
End

• Contact rhoffman_at_, 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.
  799816, 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. 113130,
  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.