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Support: JCSDA Grant “Improving analysis of tropical upper ocean conditions for forecasting”

Support: JCSDA Grant “Improving analysis of tropical upper ocean conditions for forecasting”. Bias: the unmentionable problem in ocean data assimilation* Jim Carton and Gennady Chepurin (UMD). Bias in ocean data assimilation Two-stage bias correction algorithm SODA GODAS

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Support: JCSDA Grant “Improving analysis of tropical upper ocean conditions for forecasting”

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  1. Support: JCSDA Grant “Improving analysis of tropical upper ocean conditions for forecasting” Bias: the unmentionable problem in ocean data assimilation* Jim Carton and Gennady Chepurin (UMD) • Bias in ocean data assimilation • Two-stage bias correction algorithm • SODA • GODAS • *{but, of course I’m going to talk about it}

  2. Bias is the difference between the state forecast and the true state

  3. Forecast Bias Causes • Errors in forcing • Errors in initial conditions/data coverage • Errors in physics parameterizations • Errors in numerics <.> Time-mean Annual harmonic ENSO-related etc.

  4. Bias in the ocean state estimate affects its thermodynamic influence on the atmosphere Climatological SST from ECHO2 coupled model October Cold bias Frey et al., 1997

  5. Bias in ECMWF ENSO forecasts(1987-1999) David B. Stephenson, http://www.met.rdg.ac.uk/home/swr01cac/public_html/talks/bayes3.pdf

  6. SODA grid (actual resolution is 4x)

  7. SODA flow-dependent background error

  8. Gulf Stream in SODA Hydro Observations Sea level

  9. RMS sea level variability Altimeter SODA GFDL

  10. SODA zonal vel. 0N, 140W SODA 10m OBS 50m 100m 150m

  11. SODA meridional vel. 0N, 140W SODA OBS

  12. Time-mean bias along equator “Cold tongue is too cold, while the thermocline in the central basin is too diffuse” 20C

  13. Annual cycle bias in the mixed layer Histogram of In the North Pacific Annual cycle of ML bias amp June Dec phase “The summer mixed layer is too cold, the winter mixed layer is too warm”

  14. D20 Mixed layer T Time-evolution of forecast error along equator “Forecast error is episodic, linked to ENSO” Time 

  15. Two stage algorithm to correct systematic aspects of forecast error Stage I Stage II • Alternative approaches: Saha, 1992;Thiebaux and Morone, 1997; DelSole and Hou, 1999; D’Andrea and Vautard, 2000; Griffith and Nichols 1996, 2000 • This follows: Friedland (1969) , Dee and daSilva (1998)

  16. Three-term bias forecast model ENSO-linked bias Annual cycle bias Time-mean bias

  17. along Pacific Eq Correcting time-mean bias 20C This is business as usual This is what results when time-mean bias is modeled 20C

  18. along Pacific Eq Correcting time-mean bias 20C This is business as usual This is what results when time-mean bias is modeled 20C

  19. Corr time-mean bias Correcting time-mean bias

  20. Corr time-mean bias Correcting time-mean bias

  21. Correcting annual cycle bias June Dec Business as usual Annual cycle bias correction

  22. Correcting annual cycle bias June Dec Business as usual Annual cycle bias correction

  23. Annual cycle of forecast error before correction

  24. Annual cycle of forecast error after correction After Before

  25. Correcting ENSO bias CorEOF1,SOI = 0.7 before after

  26. Correcting ENSO bias CorEOF1,SOI = 0.7 before after

  27. Thermocline depth ML temp Summary of the impact of bias correction time mean +annual cycle +ENSO variability RMS (fcst-obs)

  28. Summary • Half of the {forecast – observation} differences in high variability regions are due to bias. The largest contribution is time-mean followed by annual cycle and interannual variability. • Two-stage correction works well in addressing these. Manuscript available: {http://www.atmos.umd.edu/~carton/bias}

  29. NCEP GODAS OGCM Global MOM v.3 Data Assimilation 3D VAR Observations: XBTs TAO P-Floats Altimetry Oceanic I.C.for Coupled Model Analyzed Fields: Temperature Salinity Surface Fluxes: Momentum Heat E - P Statistical Models CCA, Markov ENSO Monitoring From Dave & Jiande

  30. NCEP GODAS products • An ocean reanalysis extending from 1979 through the present has been completed. It is forced by daily fluxes from the NCEP Reanalysis 2 and saved at 5-day intervals. • Operational ocean analyses are forced by daily fluxes from the NCEP GDAS and saved at daily intervals.

  31. Data coverageinhomogeneities,differences

  32. Zonal velocity at 0N, 110W SODA GFDL

  33. Temperature – salinity characteristics NCEP may have some problems with AAIW

  34. <Tf – To> {mean}at three depths GODAS

  35. <Tf – To> {mean} along Equator • Errors in forcing 

  36. Annual cycle of <Tf – To> at depth = 5m

  37. Annual cycle of <Tf – To> at depth = 100 m

  38. Annual cycle of <Tf – To> along equator

  39. Developing a cost function for bias analysis Where, e.g. bias estimate {This will need to be coded, but the code follows the current 3D-var code, so won’t be done ‘from scratch’}

  40. Then the state analysis cost function becomes: where: {From a programming point of view all of this is already in place.}

  41. Some conclusions • Explore data set differences • Further algorithm development • Mixed layers • Considerable potential for two-stage bias correction

  42. The End

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