Assimilation of Cloudy AMSU-A Microwave Radiances in 4D-Var

1. Assimilation of Cloudy AMSU-A Microwave Radiances in 4D-Var Una O’Keeffe Thanks to Martin Sharpe and Stephen English IPWG Workshop, Melbourne October 2006

2. Overview • Motivation • AMSU-A 23GHz and 31Ghz • Cloud liquid water incrementing operator • Assimilation set up • Assimilation results

3. Motivation • Cloud liquid water has large impact on microwave radiances • Currently low peaking AMSU-A channels are not assimilated if significant water is present • Significant data gaps due to cloud • AMSU-A window channels contain information on liquid water which is not currently exploited • Step towards assimilation of AMSR high resolution cloud and precipitation-affected radiances

4. Information on cloud liquid water RTTOV8 with clw emission RTTOV8 without clw emission NOAA-16 Obs 23GHz 31GHz

5. O-B Stats for IRclear RTTOV – 31GHz

6. O-B Stats for MWcloudy RTTOV – 31GHz

7. Cloud Incrementing Operator • Total moisture analysis variable used in 4D-Var • Need cloud incrementing operator that relates liquid water and specific humidity to the total water control variable Cx+ = Cx + KCw’ Cx= model state (q,qcl,qcf,cf) Cw’ = analysis increment (T’,p’,qT’) K = incremental transform variable between control variable space and model parameter space (uses linearised physics). Sharpe,2005

8. 1D-Var Preprocessor • Currently formulated with full field total water • Up to 8% of solutions are rejected in 1D-Var with this approach • Data volume in 3D-Var is not reduced but is biased away from cloudy areas, giving negative impact

9. Assimilation Experiment Set Up • Configuration: 3DVar, Dec05 four week period • 10 day run to generate clear air bias corrections • Cloudy obs 23+31GHz assimilation trial • assimilate NOAA-16 AMSU-A 23GHz and 31GHz • extra-tropics sea only • for all cloud conditions except for where rain flag is on

10. Analysis Increments

11. Analysis Increments

12. Impact on large scale fields fit to analysis NH | TROPICS | SH 50hPa height 850hPa humidity Most fields improved in SH 500hPa and 250hPa temp

13. Fit to observations 31GHz

14. Fit to observations 31GHz

15. Bias Correction of Cloudy Data…??? • For this test, used N16 HIRS to define ‘clear air’ and bias corrected clear air data • Operationally, also want to use N15, N17, N18 • Options: • Bias correct clear air data only – ignores large cloudy biases • Bias correct all data – may degrade clear air assimilation

16. Current Status • Testing different bias corrections • Investigations of 1D-Var rejections indicated issue with high retrieved LWP on the first iteration causing failures. A fix is now in place • Operational implementation planned for early 2007 • Plans • SSMI/SSMIS • AMSR • AMSU-B + ice incrementing operator

17. Summary • Assimilation of cloudy AMSU-A 23GHz and 31GHz data gives consistent positive impacts in SH and tropics • Some significant changes to lower level humidity cf analysis • Cloud fields improved • Unresolved issues with bias correction

18. Questions?