Development of Data Assimilation Schemes in Support of Coastal Ocean Observing Systems

Transcription

1 Development of Data Assimilation Schemes in Support of Coastal Ocean Observing Systems Zhijin Li, Yi Chao Jet Propulsion Laboratory, California Institute of Technology James C. McWilliams, Kayo Ide University of California, Los Angeles Mathematical Advancement in Geophysical Data Assimilation Banff, February 3-8,

2 Outline 1. Costal ocean observing systems 2. Assimilated observations 3. Three-dimensional variational data assimilation 4. Evaluation of analyses and forecasts 5. Observing system experiments (OSE) 6. Summary 2

3 3 Coastal Oceans

4 Integrated Ocean Observing System (IOOS): data assimilation, forecasting and adaptive sampling Observations (satellite, in situ) Data Assimilation Theoretical Understanding & Numerical Models Products Users: Managers Education & Outreach Information Observing System Design Feedback & Adaptive Sampling 4

5 Regional Coastal Ocean Observing System (RCOOS) Sea surface height SSH Velocity u/v Temperature T Salinity S 5

6 Assimilated Observations: satellite infrared SSTs NOAA GOES Infrared, High resolution Cloud contamination Microwave, Low resolution (25km) No cloud contamination NASA Aqua AMSR-E NOAA AVHRR NASA TRIMM TMI 6

7 Assimilated observations: satellite SSHs along track JASON-1 Resolution: 120km cross track, 6km along track 7

8 Real-Time High Frequency Radar Current Short distance: 100km, res of 1km, 5 MHz Long distance: 200km, res of 5km, 25 MHz 8

9 Assimilated Current Observations Shipboard Buoy Acoustic Doppler Current Profiler (ADCP) Bottom 9

10 Integrated Ocean Observing Systems * T/S profiles from gliders * Ship CTD profiles * Aircraft SSTs * AUV sections 10

11 Modeling Approach 15-km 5-km 1.5-km Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal 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

12 Model Inputs Surface wind stress Precipitation Heat fluxes Land water runoff Topography Tides 12 (Royer, 2005)

13 Tides Tide Gauge ROMS Simulation HF Radar Obs 13 Sea Surface M2 Tidal Currents

14 ROMS Analysis and Forecast Cycle: Incremental 3DVAR 3-day forecast x f 6-hour forecast Initial condition x a 6-hour assimilation cycle Time 14 Aug.1 00Z Aug.1 06Z Aug.1 12Z Aug.1 18Z Aug.2 00Z

15 Data Assimilation Formulation Variational methods (3Dvar/4Dvar): prescribed B optimization algorithm Sequential methods (Kalman filter/smoother) dynamically evolved B analytical solution 15

16 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 16

17 Inhomogeneous and anisotropic 3D Global Error Covariance Cross-shore and vertical section salinity correlation SSH correlations Kronecker Product 17

18 Construction of 3D Corelations with Kronecker Product Kronecker product Positive definiteness Cholesky factorization Computational efficiency (Li et al. 2008) 18

19 Toward a Relocatable ROMS Forecasting System: Demonstration for Prince William Sound, Alaska 9-km 3-km 1-km 19

20 20 Inhomogeneity and Anisotropy

21 Construction of Correlation Matrix C Constructed locally a, b are two locations (e.g., Cummings, 2005) Schur product 21

22 3DVAR: Weak Geostrophic Constraint Geostrophic balance Geostrophic sea surface level ageostrophic streamfunction and velocity potential 22

23 Forecast skills: AOSN-II Forecast Correlation ",-../0123-4!(+!('!(*!(&!() 23! "# #$ %& 5-./, /79:;<=> $'

24 Observing System Experiment (OSE): Glider Data Denial Experiment Temperature RMS Error Salinity w/o CalPoly glider with CalPoly glider CalPoly SIO WHOI 24 1 st week 2 nd week

25 Impact of HF Radar HF radar ROMS without HF radar data assimilation ROMS with HF radar data assimilation 25

26 Real-Time SCCOOS Data Assimilation and Forecasting System 26

27 Observability Model configurations: Grids 280 by 400 Level 40 Averaged decorrelation scale: Horizontal: 20km Vertical: Complex structure Observation availability: HF radar surface currents:10% coverage near shore Glider T/S profiles: several daily Satellite SSTs: cloudless days Ship CDT: survey monthly to seasonally ADCP: survey monthly to seasonally 27

28 Summary A coastal ocean observing system requires a data assimilation and forecasting system Tremendous progresses have been made in observations A developed data assimilation system has demonstrated forecast skills. Limited numbers of observations will be a continuing challenge in coming years. Significant model biases exist 28