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what had been done in the past 2 weeks .

what had been done in the past 2 weeks . (20060131~20060216) Jiqin Zhong 1. Compared the analysis increment using WRF 12 hour forecast as background with that using AVN 12 hour forecast as background field.

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what had been done in the past 2 weeks .

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  1. what had been done in the past 2 weeks. (20060131~20060216) Jiqin Zhong 1. Compared the analysis increment using WRF 12 hour forecast as background with that using AVN 12 hour forecast as background field. 2. Reduced the observation error to check how analysis increment react. 3. Made experiments to tune observation errors for sfc_assi=2 with which we could get equivalent analysis increment as that got with sfc_assi=1 with original observation errors. 4. Continue the EXP15 and EXP16.

  2. avn12f: AVNA －> WRF12F －> WRF －> WRFVAR Use AVN analysis as initial field to make 12h forecast with WRF model. Then use that WRF 12h forecast as background field to get analysis increment. wrf12f: AVN12F －> WRF －> WRFVAR Use AVN 12h forecast field as background to get analysis increment directly avn12f-aws-t －> the first experiment type with only AWS temperature observation assimilated. avn12f-aws-p －> the first experiment type with only AWS pressure observation assimilated. avn12f-aws-v －> the first experiment type with only AWS wind observation assimilated. avn12f-gps-pwv －> the first experiment type with only GPS PWV observation assimilated wrf12f-aws-t －> the second experiment type with only AWS temperature observation assimilated. wrf12f-aws-p －> the second experiment type with only AWS pressure observation assimilated. wrf12f-aws-v －> the second experiment type with only AWS wind observation assimilated. wrf12f-gps-pwv －> the second experiment type with only GPS PWV observation assimilated

  3. Temperature analysis increment (sigma=0.998) obs err=2C Avn12f-aws-t (left), wrf12f-aws-t(right)

  4. Pressure analysis increment (sigma=0.998) obs err=100pa Avn12f-aws-p (left), wrf12f-aws-p(right)

  5. U component analysis increment (sigma=0.998) obs err=1.1m/s Avn12f-aws-v (left), wrf12f-aws-v(right)

  6. V component analysis increment (sigma=0.998) obs err=1.1m/s Avn12f-aws-v (left), wrf12f-aws-v(right)

  7. Water mixing ratio analysis increment (sigma=0.998) Avn12f-GPS-pwv (left), wrf12f-GPS-pwv(right)

  8. Summary Most variables’ analysis increment with avn 12h forecast as background field are bigger than that with wrf 12h forecast as background field but temperature. It looks like the latter is more reasonable than the former.

  9. Temperature analysis increment (sigma=0.998) Avn12f-aws-t obs err=2C(left), obs err=1C(right)

  10. U component analysis increment (sigma=0.998) Avn12f-aws-v obs err=1.1m/s (left), obs err=0.5m/s(right)

  11. V component analysis increment (sigma=0.998) Avn12f-aws-v obs err=1.1m/s (left), obs err=0.5m/s(right)

  12. Summary: Reduced observation error generated bigger analysis increment Experiments should be done to find how much the observation error value is to reduced for sfc_assi=2 with which we could get equivalent analysis increment as that got with sfc_assi=1 and original observation error.

  13. For case 2005080200, we can get the close increment when sfc_assi set to 1 and 2 while observation error reduced : obs_t 2 C －> 1.3 C obs_v 1.1 m/s －> 0.9 m/s obs_p 100 pa obs_rh 10% －> 6% For another case 2005060100, things changed. Further experiments should be done to get the appropriate observation error with which make observation work most effectively.

  14. All experiments listed following are within the period of 050601~050607, twice forecast one day and the valid forecast time is 48 hours. The verification is the average of all forecast scores within that period. EXP11: AVN12F as FG, MM5 model, WRFVAR (Passed=4, var_scaling=1, len_scaling=1, check_rh=2, sfc_assi=1 t_err=2C, p_err=100pa, v_err=1.1m/s, rh_err=10%) Exp15: AVN12F as FG, MM5 model, WRFVAR (Passed=4, var_scaling=1, len_scaling=1, check_rh=2, sfc_assi=2 t_err=2C, p_err=100pa, v_err=1.1m/s, rh_err=10%) Exp16: AVN12F as FG, MM5 model, WRFVAR (Passed=4, var_scaling=1, len_scaling=1, check_rh=2, sfc_assi=2 t_err=1.3C, p_err=100pa, v_err=0.9m/s, rh_err=6%)

  15. In the left figure *_bj means score of only grids in Beijing area in the 27km domain In the right figure*_fullgrid means score of all grids in the 27km domain The same within the figures following

  16. Summary: As the scores of different element shows, the forecast performance with sfc_assi option set to 1 is better that that with sfc_assi option set to 2. Additionally, the reduced observation error, which are obtained through only one case and one aws station data, do not impact the forecast performance obviously.

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