Research and development on satellite data assimilation at the Canadian Meteorological Center

1. Research and development on satellite data assimilation at the Canadian Meteorological Center L. Garand, S. K. Dutta, S. Heilliette, M. Buehner, and S. MacPherson World Weather Open Science Conference 2014 Montreal, Qc August 16-21, 2014

2. Outline • Current data assimilation context • Progress on the assimilation of surface sensitive infrared radiances over land • Progress on the use of inter-channel error correlations • Observing System Simulation Experiments capability • Conclusion

3. New data assimilation context EC moving to ensemble-variational (EnVar) system with: • Flow-dependent background errors including surface skin temperature correlations with other variables • Analysis grid at 50 km, increments interpolated to model 25 km grid, 80 levels • ~140 AIRS and IASI channels assimilated: many sensitive to low level T, q, and Ts. • Assimilation of North America ground-based GPS (impacts humidity analysis)

4. Ensemble spread of Ts (Jan-Feb 2011) 00 UTC 06 UTC Maximum ~15h local In SH (summer) Maximum at night Tibetan area (winter) Ts background error over land in 4Dvar is Constant: 3 K. In EnVar background Error is 50% ENKF, 50% static (NMC) 12 UTC 18 UTC

5. Assimilation of surface-sensitive IR radiances over land • Key challenges: • Reliable cloud mask • Spectral emissivity definition • Highly variable topography • Radiance bias correction • Background and observation error definition

6. Approach guided by prudence • Assimilate surface sensitive radiances under restrictive conditions • over land and sea ice: • Estimate of cloud fraction < 0.01 • High surface emissivity (> 0.97) • Relatively flat terrain (std of height < 100 m at 50 km scale) • Diff between background Ts and rough retrieval < 4 K • Radiance bias correction approach: • For channels flagged as being surface sensitive, use only ocean • data to update bias coefficients.

7. Limitation linked to topography Criterion used: local STD of topography < 50 m (on 3X3 ~50 km areas) RED: accepted, white std > 100 m, blue 100>std>50 m

8. Limitation linked to surface emissivity Surface emissivity AIRS ch 787 Emissivity based on CERES land types (~15 km) + weighted average for water/ice/snow fraction. Accept only emissivity > 0.97 ; Bare soil, open shrub regions excluded.

9. Assigned observation error to radiances Assigned= f std(O-P) Desroziers = <(O-P)(O-A)> 15.0mm 4.13mm 15.3mm 4.50mm f typically in range1.6-2.0

10. First attempt: negative impact in regions 60-90 N/S 00 hr 72hr Possible cause: cloud contamination Risk reduction: no assimilation at latitudes > 60 deg.

11. Second attempt with added constraints • Exclude latitudes > 60 deg • Limit gradient of topography to 50 m (previously 100m) Strong sensitivity of this parameter to std(O-B) of surface channels

12. Temp std difference vs lead time NH-Extra_trop vs ERA Interim vs own analysis Consistent positive impact vs ERA Interim and own

13. T std diff (CNTL-EXP) vs ERA-Interim Tropics SH Extratropics

14. Time series of T std diff at 925 hPa CNTL EXP NH extratropics Tropics EXP superior to CNTL 55-65% of cases at days 3-5

15. 850 hPa Temp anomaly corr. CNTL EXP NH extratropics Tropics

16. 120-h N-Extr vs ERA-Interim CNTL EXP

17. Validation vs raobs 120-hFEB-Mar 2011 (59 days) CNTL EXP North America Europe U V U V GZ T GZ T

18. Added yield: about 15%(for surface sensitive channels) CNTL EXP Number of radiances assimilated for surface channel AIRS 787 CNTL: ~1400/6h EXP: ~1600/6h Region: world, EXP excludes surface-sensitive channels at latitudes > 60 Radiance thinning is at 150 km

19. R&D on Inter-channel error correlations • Approach based on Desroziers et al. 2005. It allows for a simple evaluation of the R matrix using assimilation by-products (O-A) and (O-B) for channels i,j : R = <(O-A)i(O-P)j> • Bormann et al. demonstrated that this method gives similar results as other methods • No adjustable parameter

20. Known limitations • The approach makes use of several assumptions (as other methods such as Hollingsworth-Longberg): • Unbiased observations • Uncorrelated Background and Observation errors • Well specified B matrix

21. Implementing the approach • Desroziers diagnostic was applied using 21 days of quality controlled and bias corrected radiances assimilated in a reference cycle. • The resulting Desroziers matrix was symmetrized: • Estimates done for all radiances: AIRS, IASI, AMSU-A, AMSU-B, and SSM/I. • Variances on diagonal still inflated (1.6 std(O-P))2 • Added modest 3% to analysis time • Did not affect convergence of analysis cost function, using standard pre-conditioning

22. Sample diagnosed correlations Correlation structure of the symmetrized R for the 142 AIRS channels selected for assimilation High correlations for water vapor and surface channels

23. Forecast scores against ECMWF analyses(std difference CNTL – EXP) 24-h RELATIVE HUMIDITY

24. 72-hr scores against ECMWF T GZ U V

25. Temp. anomaly correlation against ECMWF analyses (500 hPa, world) Control Experiment General conclusion: Overall positive impact, Worth implementing

26. Planned Polar Communication and Weather Mission (PCW) Goal: Fill communication and meteorological observation gaps in the Arctic. 15 min Imagery above 55N. Most likely configuration: 2 satellites in highly elliptical orbit Status: Awaiting approval from government Could operate by 2021 Meteo instrument: Similar to ABI or FCI A possible 2-sat HEO system

27. PCW would fill AMV gaps at high latitudes: what impact to expect? Example of current AMV coverage per 6-h from 10 sources

28. Impact on NWP based on OSSE(ECMWF Nature Run used as “truth”) OSSE showing significant impact of PCW AMVs filling gap in polar areas Garand et al., JAMC, 2013 Positive impact (blue) at 120-h In both polar regions from 4 satellites

29. Conclusion • R&D assimilation now benefiting from En-Var system. • Encouraging results on assimilation of surface sensitive IR radiances • over land. Next step is to relax limitation on surface emissivity. • Overall positive impact considering inter-channel error correlation for • all radiances. Plan is to implement along with Cris and ATMS in 2015. • OSSE capability developed at EC, and used to support PCW