Development of Data Assimilation Schemes in Support of Coastal Ocean - - PowerPoint PPT Presentation

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Development of Data Assimilation Schemes in Support of Coastal Ocean - - PowerPoint PPT Presentation

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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

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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, 2008

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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
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Coastal Oceans

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Integrated Ocean Observing System (IOOS):

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

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Regional Coastal Ocean Observing System (RCOOS)

  • Sea surface height SSH
  • Velocity u/v
  • Temperature T
  • Salinity S
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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

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Assimilated observations: satellite SSHs along track

JASON-1 ASON-1

Resolution: 120km cross track, 6km along track

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Real-Time High Frequency Radar Current

Short distance: 100km, res of 1km, 5 MHz Long distance: 200km, res of 5km, 25 MHz

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Assimilated Current Observations

Acoustic Doppler Current Profiler (ADCP)

Bottom Shipboard Buoy

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Integrated Ocean Observing Systems

* T/S profiles from gliders * Ship CTD profiles * Aircraft SSTs * AUV sections

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Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal

12-km

Multi-scale (or “nested”) ROMS modeling approach is developed in

  • rder to simulate the 3D
  • cean 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

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Model Inputs

  • Surface wind stress
  • Precipitation
  • Heat fluxes
  • Land water runoff
  • Topography
  • Tides

(Royer, 2005)

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Tides

Sea Surface M2 Tidal Currents

ROMS Simulation HF Radar Obs

Tide Gauge

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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

xa

xf

3-day forecast

6-hour assimilation cycle

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Data Assimilation Formulation

  • prescribed B
  • optimization algorithm

Variational methods (3Dvar/4Dvar): Sequential methods (Kalman filter/smoother)

  • dynamically evolved B
  • analytical solution
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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
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Inhomogeneous and anisotropic 3D Global Error Covariance

Cross-shore and vertical section salinity correlation SSH correlations

Kronecker Product

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Construction of 3D Corelations with Kronecker Product

  • Positive definiteness
  • Cholesky factorization
  • Computational efficiency

(Li et al. 2008)

Kronecker product

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

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Inhomogeneity and Anisotropy

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Construction of Correlation Matrix C

Constructed locally a, b are two locations (e.g., Cummings, 2005) Schur product

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3DVAR: Weak Geostrophic Constraint

Geostrophic balance Geostrophic sea surface level ageostrophic streamfunction and velocity potential

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Forecast Correlation Forecast skills: AOSN-II

! "# #$ %& !() !(& !(* !(' !(+ " ,-../0123-4 5-./,1627238/79:;<=>

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Observing System Experiment (OSE): Glider Data Denial Experiment Temperature Salinity

1st week 2nd week CalPoly SIO WHOI w/o CalPoly glider with CalPoly glider

RMS Error

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

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

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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

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Summary

  • A coastal ocean observing system requires a data

assimilation and forecasting system

  • Tremendous progresses have been made in
  • bservations
  • 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