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data assimilation on a two-layer QG channel model

data assimilation on a two-layer QG channel model. MPO624 final project Ting-Chi Wu. Data assimilation (1/2). The “Forecast” will be the “First Guess” of the next time step. Objective analysis. Data assimilation (2/2). DA cycle. Error = RMS of (DA run – Truth run)

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data assimilation on a two-layer QG channel model

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  1. data assimilation on a two-layer QG channel model MPO624 final project Ting-Chi Wu

  2. Data assimilation (1/2) • The “Forecast” will be the “First Guess” of the next time step. Objective analysis

  3. Data assimilation (2/2) DA cycle Error = RMS of (DA run – Truth run) DA run starts from t=50day, but Truth run starts from t=100day

  4. 2-layer QG channel model • Potential vorticity equation • One variable: streamfunction ~29km ~50km First guess (Background) : day 50 of true run Observation : day 100 of true run

  5. Experiments • DA run: only assimilate upper layer • Direct insertion (no objective analysis) • Int=1day • Int=2day • Int=5day • Optimum Interpolation • Div=2 gridpoints • Div=3 gridpoints • Div=4 gridpoints • Div=5 gridpoints

  6. Direct insertion

  7. With random error 5 % of average value

  8. Optimum Interpolation (1/2) Observation True value on gridpoint Analyzed value on gridpoint Use correlation instead of covariance

  9. Optimum Interpolation (2/2) Model gridpoint observation

  10. Optimum Interpolation (2/2) For every gridpoint, pick 8 nearest observations Model gridpoint observation

  11. With Objective Analysis • Model gridpoints: • X=128; Y=65; Points: 8320

  12. After OA (1/3) observation First guess/background Different spatial intervals

  13. After OA (2/3)

  14. After OA (3/3)

  15. Future work • Pick another time-period • Apply other assimilation scheme

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