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Observational Data Source Impacts In The NCEP GDAS

Observational Data Source Impacts In The NCEP GDAS. Mr. Kevin Cooley CIO & Director Central Operations, NCEP. Sponsored by JCSDA and NPOESS IPO. Introduction. Contributors Dr. Stephen Lord, Director, NCEP Environmental Modeling Center Dr. Tom Zapotocny, University of Wisconsin

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Observational Data Source Impacts In The NCEP GDAS

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  1. Observational Data Source Impacts In The NCEP GDAS Mr. Kevin Cooley CIO & Director Central Operations, NCEP Sponsored by JCSDA and NPOESS IPO

  2. Introduction • Contributors • Dr. Stephen Lord, Director, NCEP Environmental Modeling Center • Dr. Tom Zapotocny, University of Wisconsin • Tom.Zapotocny@ssec.wisc.edu • Dr. James Jung, Joint Center for Satellite Data Assimilation (JCSDA) • Jim.Jung@noaa.gov • Organizations • NCEP Environmental Modeling Center • Joint Center for Satellite Data Assimilation

  3. Introduction (Cont.) • Evaluation of current observing systems provides • An important baseline for observing system assessment and planning • Useful information for tuning and improving operational system • JCSDA • Preparing for future observing systems (METOP, NPP, NPOESS) • Assessment of current systems observing system components (at operational resolution) • Focus on satellite data • Observing System Simulation Experiments (OSSEs) for advanced instruments

  4. Introduction (Cont.) • NCEP Global Forecast System (GFS) • SSI version scheduled for operational implementation • Includes ability to assimilation AIRS data • Operational forecast model resolution • T254L64 to 84 h • T170L42 to 180 h • T126L28 to 360 h • Forecasts at 00Z only • Operational data cutoffs (except for new instruments)

  5. GFS Experimental Setup • Two Time Periods • 45 day runs • 15 Jan 2003 – 15 Feb 2003 • 15 Aug 2003 – 20 Sep 2003 • Control • All operational observations • Includes 3 AMSU configuration • AQUA observations not included • Data Denials • All AMSU • All HIRS • AIRS • Quikscat • GOES Atmospheric Motion Vectors (AMVs) • TRMM (August-Sept. 2004 only) • Fields archived for further analysis

  6. Winter Case Results Sponsored by JCSDA and NPOESS IPO

  7. Data Assimilation Impacts in the NCEP GDAS (cont) AMSU and “All Conventional” data provide nearly the same amount of improvement to the Northern Hemisphere.

  8. Impact of AMSU and HIRS on Global Temperature Forecasts RMS Forecast Impact

  9. Impact of AMSU and HIRS on Global Zonal Wind Forecasts RMS Forecast Impact

  10. Impact of AMSU and HIRS on Global Humidity Forecasts RMS Forecast Impact

  11. AMSU: 0.5 day improvement at 5 days No HIRS N. Hemisphere 500 mb ht anomaly correlation

  12. AMSU: 0.75 day improvement at 5 days No HIRS S. Hemisphere 500 mb ht anomaly correlation

  13. No AMSU The REAL problem is Day 1 No HIRS Tropics 850 mb Vector (F-A) RMS

  14. No AMSU The REAL problem is Day 1 No HIRS Tropics 200 mb Vector (F-A) RMS

  15. No Quikscat

  16. Summer Case Results Sponsored by JCSDA and NPOESS IPO

  17. HIRS Northern Hemisphere Summer vs Winter Impact N. Hemisphere 500 mb ht anomaly correlation NH Summer N. Hemisphere 500 mb ht anomaly correlation NH Winter Larger HIRS impact In Northern Hemisphere in summer than in winter

  18. HIRS Northern Hemisphere Summer vs Winter Impact N. Hemisphere 500 mb ht anomaly correlation NH Summer N. Hemisphere 500 mb ht anomaly correlation NH Winter Larger HIRS impact In Northern Hemisphere in summer than in winter

  19. Impact on Hurricane Track Forecasts AMSU HIRS GOES AMV Quikscat TRMM Sponsored by JCSDA and NPOESS IPO

  20. Satellite data ~ 10-15% impact

  21. TRMM impact < 5% Inconsistent with AMSU results at 12hours Improved initial position error (1 km)

  22. Summary • AMSU impacts dominate all forecast variables • Northern Hemisphere, Southern Hemisphere and tropics • Up to 12 h in Northern Hemisphere at 5 days • Up to 18 h in Southern Hemisphere at 5 days • HIRS impacts largest in • Northern Hemisphere in summer • Moisture field • Satellite data cannot correct rapidly growing (24 hours) model errors in tropics

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