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1 A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems Zhijin Li and Yi Chao Jet Propulsion Laboratory Jim McWilliams.

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Presentation on theme: "1 A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems Zhijin Li and Yi Chao Jet Propulsion Laboratory Jim McWilliams."— Presentation transcript:

1 1 A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems Zhijin Li and Yi Chao Jet Propulsion Laboratory Jim McWilliams and Kayo Ide UCLA ROMS User Workshop, October 2, 2007, Los Angeles

2 2 Integrated Ocean Observing System (IOOS): Modeling, data assimilation, forecasting and adaptive sampling Theoretical Understanding & Numerical Models Products Users: Managers Education & Outreach Observations (satellite, in situ) Feedback & Adaptive Sampling Information Data Assimilation Observing System Design

3 3 Outline 1.Real-time Regional Ocean Modeling System (ROMS) 2.Three-dimensional variational data assimilation 3.Assimilated observations 4.Evaluation of analyses and forecasts 5.Observing system experiments (OSE) 6.Relocatability

4 4 Coupled with Tides Sea Surface M2 Tidal Currents ROMS Simulation HF Radar Obs RMS Error of SSHs Tide Gauge

5 5 Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal 12-km Multi-scale (or “nested”) ROMS modeling approach is developed in order to simulate the 3D ocean at the spatial scale (e.g., 1.5-km) measured by in situ and remote sensors 1.5-km 5-km 15-km Modeling Approach

6 6 Data Assimilation Observations When the numerical model is so good as its prediction is superior to the climatological (almanac) forecast Model Data Assimilation

7 7 ROMS Analysis and Forecast Cycle: Incremental 3DVAR Aug.1 00Z Time Aug.1 18Z Aug.1 12Z Aug.1 06Z Initial condition 6-hour forecast Aug.2 00Z xaxa xfxf 3-day forecast y: observation x: model 6-hour assimilation cycle

8 8 Why a There-Dimensional Variational Data Assimilation Real-time capability Implementation with sophisticated and high resolution model configurations Flexibility to assimilate various observation simultaneously Development for more advanced scheme

9 9 3DVAR: Weak Geostrophic Constraint and Hydrostatic Balance Geostrophic balance Vertical integral of the hydrostatic equation ageostrophic streamfunction and velocity potential

10 10 Inhomogeneous and anisotropic 3D Global Error Covariance Cross-shore and vertical section salinity correlation SSH correlations Kronecker Product

11 11 Assimilated observations: Satellite infrared SSTs NOAA GOES NOAA AVHRR Infrared, High resolution Cloud contamination Microwave, Low resolution (25km) No cloud contamination NASA Aqua AMSR-E NASA TRIMM TMI

12 12 Assimilated observations: Satellite SSHs along track JASON-1 Resolution: 120km cross track, 6km along track

13 13 Integrated Ocean Observing Systems  T/S profiles from gliders  Ship CTD profiles  Aircraft SSTs  AUV sections

14 14 Assimilated Current Observations Acoustic Doppler Current Profiler (ADCP) Bottom Shipboard Buoy High Frequency Radar Mapped 2D surface current

15 15 3DVAR with First Guess at Appropriate Time (FGAT) 3.5DVar If FDAT 3DVar is equivalent to 4DVar

16 16 ROMS Performance Against Assimilated Data August 2006 Mean Temperature (C) Salinity (PSU) All Gliders Mean Diff RMS Diff -0.30.30.00.75 -0.10.10.00.20

17 17 Comparison of Glider-Derived Currents (vertically integrated current) AOSN-II, August 2003 Black: SIO glider; Red: ROMS

18 18 Forecast Correlation Predictability during AOSN-II Note: because gliders are moving, one cannot estimate the persistence RMSE

19 19 Observing System Experiment (OSE) – Typically aimed at assessing the impact of a given existing data type on a system – Using existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system – Assessing the impact

20 20 Observing System Experiment (OSE): Glider Data Denial Experiment TemperatureSalinity 1 st week 2 nd week CalPoly SIO WHOI w/o CalPoly glider with CalPoly glider RMS Error

21 21 HF radar ROMS without HF radar data assimilation ROMS with HF radar data assimilation Impact of HF Radar

22 22 Southern California Coastal Ocean Observing System (SCCOOS) http://ourocean.jpl.nasa.gov/SCB Southern California Bight US WEST COAST

23 23 Real-Time SCCOOS Data Assimilation and Forecasting System http://ourocean.jpl.nasa.gov/SCB

24 24 Evaluation with HF radar velocities

25 25 Toward a Relocatable ROMS Forecasting System: Demonstration for Prince William Sound, Alaska 9-km 1-km 3-km


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