Data Assimilation in Coastal Models – Moving toward IOOS and Prediction J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, R. M. Samelson College of Oceanic and Atmospheric Sciences, Oregon State University Projects with support through CIOSS: Real-time Oregon coastal simulation system (Pilot project) (PIs: R. M. Samelson, G. D. Egbert, A. L. Kurapov; associate: S. Erofeeva) US-GLOBEC-NEP Phase IIIa (CCS): Effects of meso- and basin scale variability on zooplankton populations in the California Current System using data-assimilative, physical/ecosystem models, 2005-2008. (PIs: J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, D. B. Haidvogel (Rutgers U.), T. M. Powell (UC Berkley), E. N. Curchitser (Columbia U.)) CIOSS provides partial support for a post-doctoral research associate + Interaction with ongoing ONR, NOPP, NSF funded projects on coastal ocean/atmosphere modeling and data assimilation
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Data Assimilation in Coastal Models – Moving toward IOOS and Prediction
Data Assimilation in Coastal Models – Moving toward IOOS and Prediction J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, R. M. Samelson College of Oceanic and Atmospheric Sciences, Oregon State University. Projects with support through CIOSS: - PowerPoint PPT Presentation
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Data Assimilation in Coastal Models – Moving toward IOOS and Prediction
J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, R. M. SamelsonCollege of Oceanic and Atmospheric Sciences, Oregon State University
Projects with support through CIOSS:
Real-time Oregon coastal simulation system (Pilot project)(PIs: R. M. Samelson, G. D. Egbert, A. L. Kurapov; associate: S. Erofeeva)
US-GLOBEC-NEP Phase IIIa (CCS): Effects of meso- and basin scale variability on zooplankton populations in the California Current System using data-assimilative, physical/ecosystem models, 2005-2008. (PIs: J. S. Allen, G. D. Egbert, A. L. Kurapov, R. N. Miller, D. B. Haidvogel (Rutgers U.),
T. M. Powell (UC Berkley), E. N. Curchitser (Columbia U.)) CIOSS provides partial support for a post-doctoral research associate
+ Interaction with ongoing ONR, NOPP, NSF funded projects on coastal ocean/atmosphere modeling and data assimilation
With CIOSS support: Real-time Oregon coastal simulation system (OCS)
Coastal Ocean Data Assimilation: Long Term Goals/Vision
Develop and utilize advanced modeling and data assimilation techniques to improve scientific understanding of oceanic dynamic processes on the continental shelf and interactions of the shelf flows with the interior ocean
Transfer new computational technologies into operational nowcast/forecast systems
- correction is added in small increments every time step
,a ft t t POMν ν
( )a f ft t t t ν ν G obs Hν
matrix matching observations to state vector
- correction only to u:
-correction term is present in momentum equations
-however, equations for T, S, q2, q2l are dynamically balanced (which facilitates their term balance analysis)
- Approximate gain matrix obtained from an ensemble of model runs
Time-invariant gain matrix
Effects of ADP velocity DA: improvement in near-shore SSH time series
Assimilation of velocity observations in shelf circulation models can improve accuracy of SSH maps in the coastal zone, where altimetry is not available
comparison with coastal tide gauge data near Newport
obs, no DA, DA
SSH, surf v, no DA SSH, surf v, DA surf v, HF radar [Kosro]Flow control over Stonewall Bank(Day 166, 2001)
Effects of ADP velocity DA: improvement in near-surface salinity transport
to introduce Columbia River, salinity is assimilated at 45N
No ADP assim.ADP assimilationObserved, days 162-164 (SeaSoar - Barth et al.)
S<32 psu: effect of Columbia R.
Time series of salinity at 2.4 m, 44.2N: obs, DA, no DA
Effects of ADP velocity DA: improvement in the level and temporal variability of near-bottom turbulent dissipation rate and bottom stress
12 transects, days 139-148 (2001)
Turbulence Observations: Moum et al.
transect 1
Area-ave.
Area-ave. bottom stress
yearday, 2001
Optimal Interpolation: limitations
OI corrects the ocean state, not forcing limited control over source of model error
OI assumes time-invariant forecast error covariance (Pf), used to compute the gain matrix satisfactory performance on average over a season, but possibly difficulties predicting events (instabilities, relaxation from upwelling to downwelling, etc.). State-dependent covariance is needed.
Observations (such as satellite SSH, SST, HF radar) will generally have to be processed into maps (without spatial or temporal gaps) before using OI-DA
Variational, representer based, generalized inverse method (GIM) has potential of resolving these and some other deficiencies of OI.
Methodology has been developed for using GIM efficiently with nonlinear oceanic models [Chua and Bennett, 2001]. This technology is yet to be tried in
the context of coastal ocean circulation modeling.
To use GIM, tangent linear and adjoint models have to be developed.
Ongoing research: variational representer-based assimilation into nonlinear coastal models
Tangent Linear (TL) and Adjoint (ADJ) of ROMS have been developed by ROMS AD Group (A. Moore et al.)
We are testing these tools as they become available:
Use available version of TL-AD ROMS (initial value problem – see poster)
Construct our own shallow water TL and AD codes to learn details of GIM (forced-dissipative cases)
Transition to use of full ROMS TL/ADJ as these become available over the next year or so
Some research issues:
Assimilation in presence of frontal instabilities (nonlinearity constrains growth of instabilities in the fully nonlinear model, but not so in the TL model)
Proper linearization of open boundary conditions (e.g., radiation conditions with non-smooth switching from inflow to outflow conditions)
R. Samelson, E. Skyllingstad, N. Perlin, P. Barbour
• Improve our ability to understand and predict environmental conditions in the coastal zone, especially with regard to the use and augmentation of satellite observations of wind stress
• Improve understanding of the processes that link wind stress variations to sea-surface temperature variability and ocean circulation patterns.
Coastal Ocean Data Assimilation: Long Term Goals/Vision
Develop and utilize advanced modeling and data assimilation techniques to improve scientific understanding of oceanic dynamic processes on the continental shelf and interactions of the shelf flows with the interior ocean
Transfer new computational technologies into operational nowcast/forecast systems