Advances in the assimilation of satellite observations at the Met Office

1. Advances in the assimilation of satellite observations at the Met Office Peter Weston, JCSDA Workshop, 11th October 2012

2. Contents Current Status Recent System Changes Work in Progress Future Plans Acknowledgements: John Eyre, Bill Bell, Amy Doherty, Andy Smith, Chris Burrows, James Cotton and Katie Lean

3. Contents Current Status Recent System Changes Work in Progress Future Plans

4. Current status ISeptember 2012 • 4D-Var hybrid data assimilation system • Assimilating the following satellite observations:

5. Current status IISeptember 2012 Additionally (not shown): • Soil Moisture (ASCAT) • Cloud height/amount (SEVIRI) • SSTs (AVHRR, AMSR-E) • Sea ice (SSMIS) • Snow cover

6. Contents Current Status Recent System Changes Work in Progress Future Plans

7. Global Model Upgrades 2012 • Implemented in September (PS30): • Radiative transfer model upgrade to RTTOV-9 • Assimilation of ground GNSS ZTDs • Planned for November (PS31): • Changed hybrid DA weights • Larger ensemble size • Higher ensemble model resolution • Correlated observation errors for IASI • Variable observation errors • GPSRO observation error tuning • Improved thinning of AMVs • Assimilation of OSCAT winds • Assimilation of GPSRO data from C/NOFS

8. Correlated observation errors for IASI (see my poster) • Correlations diagnosed using Desroziers’ method • Using the full matrix in the assimilation scheme led to: • A negligible increase in processing time • A large increase in the time for convergence • Matrix was reconditioned to allow quicker convergence Larger SDs Strongest correlations

9. Variable observation errors AMSU-A 5 • Caused by uncertainties in skin temperature and surface emissivity • Modelled as a function of surface to space transmittance • Varies with scan angle • Only applied to surface sensitive temperature sounding channels (AMSU-A 4, 5 & HIRS 6, 7) • Additionally this has allowed us to: • Use AMSU-A 5 over sea ice • Accept more data from other mid tropospheric channels (AMSU-A 6, 7 and 8) over sea ice

10. GPSRO – Reduced bending angle observation errors • Above 10km the percentage error has been reduced from 2% to 1.5%. • Also, the absolute minimum error has been reduced from 6μrad to 3μrad. • This has improved forecasts verified against other observations – fairly neutral against analyses (see 24hr height improvement below).

11. GPSRO – assimilation of C/NOFS bending angle data • C/NOFS is a US military satellite – data is processed by UCAR. • It has an orbital inclination of 13°. • Data currently unavailable below ~8km. • Timeliness issues mean that much of the data won’t be assimilated. Tropics had few occultations before C/NOFS. C/NOFS Bending angle O-Bs. Similar ‘bias’ to other satellites at these latitudes.

12. AMV thinning strategy Main approach to alleviate problems with spatially and temporally correlated error (another option is superobbing). Current strategy: All geo winds thinned in 2°x 2°x 100 hPa boxes. All polar winds thinned in 200x200 km x 100 hPa boxes. Wind selected by lowest error for geo Wind selected by closest to centre of box for polar winds. Only one wind selected per box in the 6 hour time window. Main limitation (legacy of 3D VAR) • New Approach: Introduce 2-hourly temporal thinning -> 3x number of AMVs used • Make better use of hourly data available from MSG, MTSAT and GOES-13/15 (available in test mode) 2-hourly thinning Operational

13. Oceansat-2 Assimilation of OSCAT wind vectors from Indian Oceansat-2 satellite. OSCAT: Ku-band, conical scanning pencil-beam scatterometer operating at 13.52 GHz, similar in design to the QuikSCAT instrument which failed in November 2009. Became TS Joyce TS Isaac Utilising the 50-km L2B wind product produced by KNMI/OSI-SAF (http://www.knmi.nl/scatterometer) Improved global coverage of ocean surface wind vectors alongside ASCAT on Metop-A and WindSat. Wind retrieval results in ambiguous set of 2-4 wind solutions.

14. Verificationvs Obs • Hybrid DA changes • Weights • Ensemble size • Satellite changes: • Correlated errors • Variable errors • GPSRO errors • C/NOFS GPSRO • AMV thinning • OSCAT • Package

15. Contents Current Status Recent System Changes Work in Progress Future Plans

16. ATMS Channel 12 raw O-B Bias corrected • We plan to get ATMS into operations in March 2013 with: • Footprint matching AMSU and noise reduction • Channels 6-15, 18-22 • QC following treatment of AMSU Striping Strong regional bias

17. ATMS Verification

18. AIRS 172 O-B CrIS data IASI 222 O-B CrIS 88 O-B • Planned implementation: • Similar to AIRS/IASI • Use 129 channels - 72 T, 44 WV, 13 Surface, 0 SW (band 3)

19. Contents Current Status Recent System Changes Work in Progress Future Plans

20. MetOp-B AMSU-A first look

21. Thanks for listening!Any questions?

22. Backup slides

23. SSMIS Improvements • We currently assimilate SSMIS data from the F-16 instrument that is pre-processed using our original in-house SSMIS ‘PP’ pre-processing software. • We are hoping to imminently assimilate the DMSP F-18 data into the model. This data is pre-processed using the Unified Pre-Processor (UPP) at the Naval Research Laboratory and has improved data coverage. • In the F-18 data there is a strong ascending/descending bias. Therefore a new ascending/descending bias predictor has been developed and is being introduced to compensate for this bias. F18 ‘UPP’ With standard correction With additional ascending/descending correction For further details: ‘An assessment of the characteristics of SSMIS from F-16 to F-18’ (poster), Anna Booton, ITSC-18 Conference, Toulouse, March 2012.

24. Future Work • Assimilate data from more satellites: • MetOp-B • MSG-3 • MTSAT • FY-3 • Improved cloud modelling • Improved assimilation of radiances over land • Improved treatment of variable O3 & CO2 • More complete use of hyperspectral IR • Variational bias correction