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

2. © Crown copyright Met Office 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. © Crown copyright Met Office Pressure (hPa) IASI temperature Jacobians Surface-sensitive channels Large proportion of tropospheric channels! The problem

4. © Crown copyright Met Office Land surface emissivity (8.5  m) from NASA AIRS product, June-August 2008

5. © Crown copyright Met Office 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. © Crown copyright Met Office 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. © Crown copyright Met Office 1D-Var fit to observations Window channel, assuming  = 0.98 Over sea: use ISEM emissivity model (quite accurate) Over land: assume emissivity=0.98 Large mis-fit

8. © Crown copyright Met Office 0.98

9. © Crown copyright Met Office 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. © Crown copyright Met Office 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. © Crown copyright Met Office Atlas: 9.32  m (mineral signal) True Emissivity

12. © Crown copyright Met Office Retrieval: 9.32  m (mineral signal) Retrieved Emissivity

13. © Crown copyright Met Office 920 hPa temperature bias: without emissivity retrieval

14. © Crown copyright Met Office 920 hPa temperature bias: with emissivity retrieval

15. © Crown copyright Met Office Promising results from simulations…  Test with real data …but first: what about clouds?

16. © Crown copyright Met Office Need good  and Tskin to detect cloud Need knowledge of cloud to analyse sfc!

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

18. © Crown copyright Met Office 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. © Crown copyright Met Office 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 T skin biases during daytime At present data included nighttime only, and only IASI First step - improvements expected

20. © Crown copyright Met Office Conclusions Currently, low-peaking IR sounding channels are rejected over land - uncertainties in emissivity and T skin 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. © Crown copyright Met Office Questions ?