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Satellite Bias Correction for CFSRR

This research paper discusses the need for radiance bias correction in data assimilation, along with the methods and results of bias correction experiments. The paper also explores the future work and possible issues related to bias correction and assimilation.

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Satellite Bias Correction for CFSRR

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  1. Satellite Bias Correctionfor CFSRR Haixia Liu, Russ Treadon, Robert Kistler, John Derber, Suru Saha and Hua-lu Pan Nov. 7, 2007 with input from Jack Woollen, Quanhua Liu, Moorthi Shrinivas, Cathy Thiaw and others

  2. radiance used radiance NOT used …… (Reference)

  3. What’s needed to assimilate radiance? • CRTM model, CRTM coeff files • satinfo which contains the usage flag, obs error and Instrument/observation characteristics (microwave/IR, etc.) • Radiance obs bias correction is needed.

  4. Why bias correction (BC)? • RTM are subject to systematic errors. • Radiance obs contain systematic errors. • Magnitudes of these errors are comparable with radiance changes corresponding to typical errors in the T from short-range forecast model. • Try to use radiance to improve the accuracy of the T field during DA process.

  5. Scan bias (slowly evolve) Satellite Bias Correction in GSI Observation bias Air mass bias (dependent on atmospheric state) Constant Zenith angle Cloud liquid water Square of T lapse rate T lapse rate etc. is the coefficients of predictors

  6. Adaptive BC in GSI Allow temporal variation of the coefficients using data assimilation Variational bias correction VS Find the optimal b to minimize the cost function J through the variational procedure.

  7. 2 BC files are required to run gsi • Coefficients for predictor part of BC (biascr.gdas.*) • Updated within inner loop of analysis • Slowly evolving angle dependent part of BC (satang.gdas.*) • Updated in separate job step following analysis using Tb innovation file • Method: Start from guess values and run a training period. Experiments were conducted to see if this procedure can lead to convergence to the operational values (‘truth’) after the training period.

  8. Preliminary Results AMSU Blue: EXP Red: CNTL

  9. HIRS Blue: EXP Red: CNTL

  10. Scan bias and air mass mean bias compensate to each other. Combine the scan bias and mean term together to avoid the compensation between them. Remove predictor zenith angle.

  11. The coefficients for predictor cloud liquid water should not start from 0 since this term evolves very slowly. Only applied to amsua data available after 199810. Will start from average value used in operation.

  12. GSI codes and scripts ready for CFSRR • Modify GSI code and analysis script to handle pre-NOAA-14 satellites/sensors. • Modification made in GSI and link to new CRTM which considers co2 leaking in cell pressure modulator which ssu data suffers. • GSI is basically frozen for CFSRR now.

  13. Generating satinfo historical files • The satinfo historical files are generated every 6 hours from 1979 to present. The satellite/sensor/channel use flag is turned on/off in satinfo files based on the GMAO blacklist and data availability at NCEP. • Amsua_aqua channel 4 is going to be used as QC purpose rather than assimilated. Amsua_n16 chn4 is in monitor mode. • Questions: hirs2_n11 in 2nd period is extended to 20000426 and ssu_n11 is extended to 20040616 (GMAO ends using them at Feb 1999 and ERA40 ends using them in Sep 1998); ssu-n12 data is going to be used extending from 19970530 to the end of 1998.

  14. [hl1] Hirs3_n15 are not used any more by GDAS in operation. [hl2] Hirs3_n16 are not used any more by GDAS in operation. [hl3] Amsua_n17 are not used any more by GDAS in operation.

  15. [hl1]Sndr_g11 are not used any more by GDAS in operation.

  16. 4 Simultaneous Streams • Jan 1979 – Sep 1986 8 years • Apr 1986 – Oct 1993 7 1/2 years • Apr 1993 – Oct 2000 7 1/2 years • Apr 2000 – Feb 2010 10 years 6 month overlap for ocean and land spin ups Total of 33 years of Reanalysis

  17. Future work • 21 satellite bias correction spin up experiments should be done by the end of 2007. • Sensitivity of bias to the atmospheric IC. Start from r2 or gdas of the previous cycle? Tests on amsua_n15 indicate that the bias correction is not very sensitive to the IC (the current predictors chosen can identify obs bias from NWP model error.) • Possible issues related to SSU bias correction and assimilation.

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