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Report to 20 th Data Exchange Meeting by Met Office

Report to 20 th Data Exchange Meeting by Met Office. Roger Saunders with the help of Steve English Bill Bell, Mary Forsythe, Brett Candy, Mike Rennie, Adrian Jupp, Sam Pullen, Jon Taylor. Met Office Operational Models : 2007. Global ~40km ~50Level

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Report to 20 th Data Exchange Meeting by Met Office

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  1. Report to 20th Data Exchange Meeting by Met Office Roger Saunders with the help of Steve English Bill Bell, Mary Forsythe, Brett Candy, Mike Rennie, Adrian Jupp, Sam Pullen, Jon Taylor

  2. Met Office Operational Models : 2007 • Global ~40km ~50Level • North Atlantic/Europe 12km ~30level • UK 4km(1km) ~30Level • Re-locatable Defence and Civilian • Ensemble (global & regional) at half horizontal resolution • Data assimilation 4DVar 6 hour window for global and regional models

  3. SuperComputer Changes

  4. Operational data usage (Apr 2007)

  5. Satellite data delays May 2007 Main run data cutoff

  6. ATOVS Data

  7. Recent changes to use of satellite data • GPS-RO Cosmic, Champ/Grace refractivity pofiles assimilated briefly in Dec 06 and again from May 2007. • DMSP-F16 SSMIS 50GHz channels assimilated from Sept 2006 • METOP AMSU-A, MHS added in Jan 07 • NOAA-17 HIRS added in Jan 07 • Groundbased GPS IWV assimilated in NAE from April 07. • AMVs • Meteosat-8 replaced by Meteosat-9 • Meteosat-5 replaced by Meteosat-7 • MTSAT SATOB replaced by MTSAT BUFR • MODIS direct broadcast used to increase coverage

  8. Global Forecast Improvements since mid-2005 AIRS WF, ERS-2, Met-8 GPS RO, SSMIS ATOVS 3hr thinning + incr. use EARS METOP-A, NOAA-17 reintro. COSMIC intro GOES-BUFR, Scat bias correction NOAA-18 50 Levels Parallel Suite

  9. METOP Payload Denotes used Denotes In test

  10. METOP AMSU-A/MHS Antenna Corrections Met Office EUMETSAT (on EUMETCAST) No correction

  11. IASI Midwave Validation BT (K) IASI, NAST-I, SHIS Mean spectra * Correlated IASI differences with NAST-I and SHIS, shown in the plot below, can result from errors in the simulation of the radiance contributions to IASI from the atmosphere above the aircraft (~ 18 km). IASI minus NAST-I, IASI minus SHIS (using double obs-calc method) Diff (K) NAST-I: S-HIS: 0.12 K 0.20 K 0.04 K 0.03 K -0.19 K -0.08 K Wavenumber (cm-1)

  12. IASI Implementation • IASI poses huge challenges because of the volume of data • 8461 channels, 120 observations per scan • We will reduce this data volume by using only 300 channels and one in four observations • Channels selected on the basis of information content • Reduces data volume of one IASI to about the level of three ATOVS • Initially, we will use the data in a very similar way to AIRS • Sea only • Clear only (via 1D-Var cloud detection)

  13. Met Office SSMIS assimilation experiments UKMO-2SAT-3DN216: Low resolution 3D-Var test of SSMIS on top of 2 satellite system (NOAA16 & 18) UKMO-OPNS-3DN216: Low resolution 3D-Var test of SSMIS on top of 3 satellite system (NOAA15, 16 & 18) UKMO-OPNS-4DN320: High resolution 4D-Var test of SSMIS on top of 3 satellite system Implemented operationally 26th September 2006

  14. SSMIS developments • Efforts are ongoing to unify SSMIS pre-processing (combining NRL & Met Office approaches) . Expect new data stream by October 2007 (F-16 SSMIS). • Fast RT model for Zeeman split Upper Atmospheric Sounding channels available (Y. Han, NESDIS) and used to evaluate ECMWF fields to 0.01 hPa (S. Swadley, NRL & W. Bell). • Cal/Val for F-17 ongoing (NRL) expect data stream Spring 2008.

  15. AMV Satellite Status Update NESDIS and direct broadcast MODIS polar winds (Terra and Aqua) NESDIS MODIS polar winds (TERRA and AQUA) 60N Meteosat-9 Apr 07 Meteosat-7 Mar 07 MTSAT-1R BUFR Apr 07 GOES-11 18 Jul 06 Meteosat-7 Mar 07 GOES-10 GOES-12 Meteosat-8 Meteosat-5 MTSAT-1R SATOB equator 60S NESDIS and direct broadcast MODIS polar winds (Terra and Aqua) NESDIS MODIS polar winds (TERRA and AQUA) • Other changes in 2007 • Goes hourly data • Increased coverage from MSG (out to ~65N) • Storage and monitoring of FY-2C winds • GOES-10 over S. America (?) • AVHRR polar winds (?) Monitoring 22 datasets from 9 geos and 2 polar satellites

  16. MODIS direct broadcast winds • MODIS direct broadcast winds from McMurdo Station and Tromsø assimilated operationally in the Met Office model since 13th December 2006. • Provide • Partial polar coverage. • Similar quality to conventional MODIS winds. • Shorter time lag (averages 180 min rather than 280 min). • Translates into more polar AMVs in our main forecast and update runs. • Conventional Direct Broadcast • Main forecast ~18% ~45% • Update ~70% ~90%

  17. Comparison to model best-fit pressure Vector Differencei = √((ObU – BgUi)2 + (ObV – BgVi)2) 100 Model best-fit at minimum in vector difference profile. AMV pressure and model best-fit pressure agree well in this case. 200 300 400 500 Pressure (hPa) 600 700 u component 800 v component Diff 900 1000 -20 0 20 40 60 m/s

  18. Model best-fit pressure comparisons Low level GOES winds show a significant high height bias compared to model best-fit pressure (worse over sea than land). The RMS also increases as the pressure decreases from 1000 hPa to 600 hPa. GOES-12 (final product) 3rd July – 15th Aug 06 EUMETSAT winds are less affected (particularly since March 07 change), probably partly due to use of an Inversion Correction method.

  19. GPS Radio Occultation RO Missions: GPS/Met : 1995 – 2000 Ørsted : 1998 – SunSat : 1999 – SAC-C : 1999 – CHAMP: 2000 – GRACE-A/B : 2002 – COSMIC : Apr 2006 GRAS : Oct 2006

  20. Vertical profile of T wrt radiosondes Vertical profile for temperature mean error (top) and temperature RMS (bottom) in SH at 24 hr fc range. CONTROL, COSMICx6 The assimilation of GPSRO, as expected, reduces the RMS error in the uppermost troposphere and corrects model biases. Note similar patterns but smaller impact seen in NH and TR

  21. Bias and RMS as function of forecast range Temp, 250 hPa, SH Wind speed, 100 hPa, SH

  22. Differences in mean 24hr forecast T fields • GPSRO causing cooling over Antartica at 250hPa. 1D-Var solution to right supports this (1st Dec, -64 deg lat). This was frequently seen in these plots • Removing model bias?

  23. Summary • COSMIC reduces both bias and RMS error in the UTLS for T,GPH,RH and wind speed. • Dominated by improvements in SH • Information from GPSRO leads to benefits in many other fields indirectly related to T,P,Pw,GPH through model physics. Perhaps changes in humidity can cause large scale wind/circultation adjustments (cf radiance info, see McNally 1994) • Zonal means show important changes in vertical structure of temperature above south pole, perhaps correcting biases in model. • Large average changes in pressure, wind fields can be seen over high latitudes

  24. Ground-based GPS • Uses standard GPS navigation signals and standard geodetic-quality receivers • Atmospheric zenith total delay (ZTD) included in position solution • Information on ‘dry’ (ZHD) and ‘wet’ (ZWD) components • IWV = (ZTD-ZHD)/k = ZWD/k (k ~6.5) • European collaboration via EUMETNET E-GVAP (& previously EU COST-715 & TOUGH) • Semi-operational hourly data downloads from 500+ stations over Europe • Processed to Total Zenith Delay in <2 hours

  25. NRT GPS network • COST 716/ TOUGH projects developed NRT GPS network, E-GVAP continues • Lots of different processing centres • Some overlaps- i.e. more than 1 processor for a given station • Met Office processes UK sites in- house

  26. Monitoring: http://www-nwp/~frmj/Ground_GPS/GPS_Monitor • Black- NAE model • Purple- ‘operational’ • Orange- test machine • Green- test machine • This is a sensible way to make a change!

  27. NWP impacts Green = improvement in FC Red = degradation in FC Also small improvements in visibility, cloud and precipitation ETS. Overall weighted score showed 1.85% improvement

  28. Use of NESDIS IMS Snow NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS) • GEO (GOES, Meteosat, MTSAT) • LEO (AVHRR, MODIS, SSM/I, AMSU) • Derived products (e.g. USAF Now and Ice Analysis Product) • In situ data • Analyst • Daily, 4km resolution, NH • Polar stereographic 6144 X 6144 array • GRIB from NCEP – snow cover (0 or 100), ice (0 or 1) • Received on Radsat, to MetDB (snow only) in GRIB format • Data extracted and processed within SURF system

  29. An example 18/12/06 00Z Modifications to model snow amount field

  30. NWP Satellite Application Facility • In first year of operational phase up to 2012 • Major deliverables are: • AAPP (ATOVS/IASI/AVHRR direct readout software) • RTTOV (Fast radiative transfer model) • SSMIS preprocessing package • 1DVAR retrieval packages (METO/ECMWF versions) • Satellite data monitoring (Radiance, AMVs, O3) • Scatterometer processor • Advanced sounder preprocessing software • Reports on many aspects of satellite data • Also involved in GRAS (GPS RO) SAF

  31. Work in progress….. • Add HIRS, IASI, ASCAT and GRAS from METOP • Investigating AMV height assignments and observation errors • Assimilation of MSG cloud information • Extend use of SSMIS to window/wv channels Longer term…. • WINDSAT • AMSR-E precipitation • Scatterometer soil moisture • ADM doppler lidar winds • NPP

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