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MTG-IRS OSSE on regional scales

MTG-IRS OSSE on regional scales. Xiang-Yu Huang, Hongli Wang, Yongsheng Chen and Xin Zhang National Center for Atmospheric Research, Boulder, Colorado, U.S.A. Stephen A. Tjemkes, Rolf Stuhlmann EUMETSAT, Darmstadt, Germany. Contents. The OSSE configuration The nature run

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MTG-IRS OSSE on regional scales

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  1. MTG-IRS OSSE on regional scales Xiang-Yu Huang, Hongli Wang, Yongsheng Chen and Xin Zhang National Center for Atmospheric Research, Boulder, Colorado, U.S.A. Stephen A. Tjemkes, Rolf Stuhlmann EUMETSAT, Darmstadt, Germany Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 1

  2. Contents The OSSE configuration The nature run Simulated (conventional and MTG) observations Calibration experiments Data assimilation and forecast results 4D-Var results Summary Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 2

  3. The OSSE Configuration • Nature run model: MM5 • Assimilation model: WRF • Data assimilation scheme: WRF 3D-Var (4D-Var) • Selected cases: • International H2O Project (IHOP, 13 May - 25 June 2002) • Southern Great Plains of US http://catalog.eol.ucar.edu/ihop/catalog/missions.html Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 3

  4. OSSE setup Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 4

  5. IHOP Case Three convection cases are selected from 11 June 2002 to 16 June 2002: • 11 June: Dryline and Storm • 12 June: Dryline and Storm • 15 June: Severe MCS Map illustrating the operational instrumentation within the IHOP_2002 domain (Weckwerth et al. 2004) Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 5

  6. Nature run configuration • Nature model: MM5 • Grid points: 505X505X35 • Horizontal resolution: 4Km • Time step: 20s • Physics parameterizations: • Reisner 2 microphysics • No cumulus parameterization • MRF boundary layer • Initial and Lateral boundary condition: • 6-hourly ETA model 40-km analyses (phase I: GFS analyses) • ~ 220 minutes with 256 CPUs model domain Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 6

  7. Case A: 11 June Case observed 6-h rainfall simulated 6-h rainfall 0600 UTC 12 Jun 0600 UTC 12 Jun The observation is on Polar Stereographic Projection Grid. The simulated rainfall is on Lambert Projection Grid. The color scales are different. Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 7

  8. Case B: 12 June Case 0600 UTC 13 Jun 0600 UTC 13 Jun observed 6h-rainfall simulated 6h-rainfall Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 8

  9. Case C: 15 June Case 0000 UTC 16 Jun 0000 UTC 16 Jun observed 6h-rainfall simulated 6h-rainfall Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 9

  10. Simulated Dataset Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 10 WRF-Var is employed to produce simulated conventional observations (NCEP ADP Upper Air sounding, Surface Observation, AIRCAR/AIRCFT and Satellite wind data ) • Simulated observations use the actual locations and times • Use realistic observation errors

  11. Simulated Dataset NCEP ADP Upper Air sounding NCEP ADP Surface Observation Example of Simulated Data distribution within the time window: 2300 UTC 11 Juneto0100 UTC 12 June 2002 33 Sounding 501 Surface Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 11

  12. Simulated Dataset NCEP ADP AIRCAR/CRAFT NCEP ADP Satellite wind Example of Simulated Data distribution within the time window: 2300 UTC 11 June to 0100 UTC 12 June 2002 748 ACAR/CFT 717 SATOB Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 12

  13. MTG-IRS retrieved profiles Example of Simulated Data distribution within the time window: 2300 UTC 11 June to 0100 UTC 12 June 2002 11679 MTG-IRS RP Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 13

  14. Error statistics for MTG-IRS q T Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 14

  15. Calibration runs • Generate pseudo radiosonde observations from the nature run. • Exp 1. No-obs. • Exp 2. Pseudo-obs. Data assimilation experiment using the pseudo observations. • Exp 3. Real-obs. Data assimilation experiment using real (radiosonde) observations. Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 15

  16. Difference in T (K), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At 12 h FCST At analysis time SOP: Simulated Observation Profiles; ROP: Real Observation Profiles Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 16

  17. Difference in q (g/kg), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At analysis time At 12 h FCST SOP: Simulated Observation Profiles; ROP: Real Observation Profiles Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 17

  18. Difference in u (m/s), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At analysis time At 12 h FCST Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 18

  19. Experiments design (3D-Var) Forecast model: WRF V3.0 Grid points: 169X169X35 Horizontal resolution: 12 Km Time step: 60s Physics parameterizations: WSM6 microphysics Grell cumulus paramerization MRF boundary layer Cases: 2002-06-11 12Z to 2002-06-15 12Z Data: MTG-IRS retrieved profiles + SOP Verification against truth Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 19

  20. Lists of Experiments Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 20

  21. RMSE: 6hourly cycling results Analysis Time 12 h FCST Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 21

  22. RMSE: 6hourly & 1hourly cycling results Analysis Time 12 h FCST Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 22

  23. Averaged ETS and BIAS at 18 h FCST ETS BIAS Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 23

  24. Forecast model: WRF Data assimilation system: WRF 4D-Var Grid points: 169X169X23 Horizontal resolution: 12Km Time step: 60s Assimilation window: 3 hours Physics parameterizations: WSM6 microphysics New Grell cumulus paramerization MRF boundary layer Case A: 2002-06-11 18Z to 2002-06-12 12Z Case B: 2002-06-12 18Z to 2002-06-13 12Z Background: GFS analysis and forecasts Data: Retrieved profiles + SOP or “Truth” Verification against truth Experiments design (4D-Var) D1 Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 24

  25. RMS error of Case A 12 h FCST Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 25

  26. Case A at 18H FCST ETS BIAS Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 26

  27. RMS error of Case B 12 h FCST Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 27

  28. Case B at 18H FCST ETS BIAS Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 28

  29. Summary Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 29 Three storms are well reproduced in the 5 day nature run. The calibration experiment shows that the real and simulated observations have the similar impacts on the analyses increments and forecasts differences. The quality of the retrievals has been improved significantly. The forecast skill of wind, temperature and moisture forecasts is improved when MTG-IRS T and/or q profiles are assimilated. Rainfall forecast skill is improved also. In two short periods of experiments, assimilating MTG-IRS data using 4D-Var gives slightly better forecast skills over 3D-Var.

  30. Current and planned work Continue the 4D-Var experiments Other nature runs Try other cases Europe Tropics Over oceans … Assimilate wind data Huang et al: MTG-IRS OSSE MMT, 17-18 June 2008. 30

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