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Advances in the assimilation of satellite observations at the Met Office. Peter Weston, JCSDA Workshop, 11 th October 2012. Contents. Current Status Recent System Changes Work in Progress Future Plans
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Advances in the assimilation of satellite observations at the Met Office Peter Weston, JCSDA Workshop, 11th October 2012
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
Contents Current Status Recent System Changes Work in Progress Future Plans
Current status ISeptember 2012 • 4D-Var hybrid data assimilation system • Assimilating the following satellite observations:
Current status IISeptember 2012 Additionally (not shown): • Soil Moisture (ASCAT) • Cloud height/amount (SEVIRI) • SSTs (AVHRR, AMSR-E) • Sea ice (SSMIS) • Snow cover
Contents Current Status Recent System Changes Work in Progress Future Plans
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
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
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
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).
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.
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
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.
Verificationvs Obs • Hybrid DA changes • Weights • Ensemble size • Satellite changes: • Correlated errors • Variable errors • GPSRO errors • C/NOFS GPSRO • AMV thinning • OSCAT • Package
Contents Current Status Recent System Changes Work in Progress Future Plans
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
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)
Contents Current Status Recent System Changes Work in Progress Future Plans
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.
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