Assimilating infra-red sounder data over land

1. Assimilating infra-red sounder data over land John Eyre for Ed PavelinMet Office, UK Acknowledgements: Brett Candy DAOS-WG, 4th meeting, Exeter, 27-28 June 2011

2. Assimilating infra-red sounder data over land Outline • The problem • Met Office implementation: 1D-var + 4D-Var • Retrieval assimilation studies: • on simulated data • on real data • Conclusions

3. Surface-sensitivechannels Large proportion oftropospheric channels! The problem IASI temperature Jacobians Pressure (hPa)

4. Land surface emissivity (8.5m)from NASA AIRS product, June-August 2008

5. Use of IR radiances over land • Current operations: • Assume infrared emissivity = 0.98 over land • Not good enough – don’t use channels sensitive below ~ 400hPa • Options to increase data use over land • Use fixed emissivity “atlas” • Use land surface model / surface type atlas • Retrieve surface emissivity from observations

6. Met Office assimilation of AIRS and IASI radiances • 1D-Var analysis for each radiance vector: • … to estimate atmospheric profile + surface variables • … to provide: • QC on radiances passed to 4D-Var • estimate of variables not in 4D-Var control variable: • skin temperature • [temperature profile above model top] • cloud top height, cloud fraction • ***NEW*** – surface emissivity variables • Radiances and 1D-Var-retrieved variables passed to 4D-Var

7. Over land: assume emissivity=0.98 Large mis-fit Over sea: use ISEM emissivity model (quite accurate) 1D-Var fit to observationsWindow channel, assuming  = 0.98

8. 0.98

9. Simulation experiment: retrievals from simulated AIRS radiances (1) Simulated radiances: • simulated using RTTOV radiative transfer model • 13495 atmospheric profiles from ECMWF ERA40 dataset • surface emissivity from UWisc/CIMSS IR emissivity atlas (2006 data used) • derived from MODIS observations, fitted to laboratory spectra • independent of training dataset • simulated observation errors

10. Simulation experiment: retrievals from simulated AIRS radiances (2) 1D-Var retrievals: • 12 emissivity PCs in retrieval vector • first guess emissivity = 0.98 • emissivity retrieved simultaneously with T, q and cloud • idealised case: • using all AIRS channels • clear sky only

11. Atlas: 9.32 m (mineral signal) True Emissivity

12. Retrieval: 9.32 m (mineral signal) Retrieved Emissivity

13. 920 hPa temperature bias:without emissivity retrieval

14. 920 hPa temperature bias:with emissivity retrieval

15. Promising results from simulations…  Test with real data …but first: what about clouds?

16. Need good  and Tskin to detect cloud Need knowledge of cloud to analyse sfc!

17. Use a piori emissivity atlas? Use a priori emissivity atlas Need good  and Tskin to detect cloud Need knowledge of cloud to analyse sfc!

18. First-guess emissivity:a global IR emissivity atlas • CIMSS/Univ. Wisconsin IR emissivity atlas • based on MODIS products • emissivities fitted to high-spectral-resolution laboratory measurements • monthly variability, high spatial resolution • becoming widely used • Other atlases available from other sources • Products from AIRS, IASI, …

19. Global NWP trials • Bottom line: impact on Met Office “global NWP index” • Winter: +0.0 vs obs, -0.1 vs anal • Summer: +0.1 vs obs, +0.3 vs anal • Sufficiently +ve to include in next operational change (July 2011) • However: • Problems with model Tskin biases during daytime • At present data included nighttime only, and only IASI • First step - improvements expected

20. Conclusions • Currently, low-peaking IR sounding channels are rejected over land - uncertainties in emissivity and Tskin • Improvements implemented via 1D-Var: • analysis of spectrally-varying surface emissivity • “first-guess” global emissivity atlas • Poor fit to observations over daytime deserts • possible systematic biases in model surface temperature

21. Questions ?