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 Organizations
Dr. Stephen Lord, Director, NCEP Environmental Modeling Center
Dr. Tom Zapotocny, University of Wisconsin
Dr. James Jung, Joint Center for Satellite Data Assimilation (JCSDA)
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 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. on Global Temperature Forecasts
Impact of AMSU and HIRS
on Global Temperature Forecasts
RMS Forecast Impact

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

10. on Global Humidity Forecasts
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. The REAL problem is Day 1 No AMSU No HIRS Tropics 850 mb Vector (F-A)
RMS

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

15. No Quikscat

17. Summer Case Results
Sponsored by JCSDA and NPOESS IPO

18. Summer vs Winter Impact
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. Summer vs Winter Impact
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

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

21. Satellite data
~ 10-15% impact

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

23. 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