1 / 15

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)

hinto
Télécharger la présentation

data assimilation on a two-layer QG channel model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related