Joint Center for Satellite Data Assimilation: Mission, Vision, and Goals

1. Overview • The Joint Center for Satellite Data Assimilation • Composition • Mission, Vision, and Goals • Recent Accomplishments • Role for GPSRO • Near Term Priority for JCSDA • Outlook/Summary

2. JCSDA Composition • NOAA • NESDIS/ORA • NWS/NCEP/EMC • OAR • NASA • GMAO • DoD • NRL • AFWA • ARL

3. JCSDA Mission and Vision • Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather and climate analysis and prediction models • Near-term Vision: A weather and climate analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations • Long-term Vision: An environmental analysis and prediction community empowered to effectively use the integrated observations of the GEOSS

4. Goals – Short/Medium Term • Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction models • Develop the hardware/software systems needed to assimilate data from the advanced satellite sensors • Advance common NWP models and data assimilation infrastructure • Develop a common fast radiative transfer system (CRTM) • Assess impacts of data from advanced satellite sensors on weather and climate analysis and forecasts (OSEs,OSSEs) • Reduce the average time for operational implementations of new satellite technology from two years to one

5. Expected Results/Benefits: Near-term • Improved weather and climate analyses and predictions • Better climate time series • Greater return on investment by earlier and enhanced use of space assets for civilian and military environmental prediction • Better planning of future satellite instruments

6. Some Major Accomplishments • Common assimilation infrastructure at NOAA and NASA • Common NOAA/NASA land data assimilation system • Interfaces between JCSDA models and external researchers • Community radiative transfer model-Significant new developments, New release June/July • Snow/sea ice emissivity model – permits 300% increase in sounding data usage over high latitudes – improved polar forecasts • Advanced satellite data systems such as EOS (MODIS Winds, Aqua AIRS, AMSR-E) tested for implementation -MODIS winds, polar regions - improved forecasts. Current Implementation -Aqua AIRS - improved forecasts. Current Implementation • Improved physically based SST analysis • Advanced satellite data systems such as -DMSP (SSMIS), -CHAMP GPS being tested for implementation • Impact studies of POES AMSU, Quikscat, GOES and EOS AIRS/MODIS with JCSDA data assimilation systems completed.

7. Figure 4. Impact of sea ice and snow emissivity models on the GFS 24 hr. fcst. at 850hPa. (1 Jan. – 15 Feb. 2004); the pink curve shows theACC with new snow and sea ice emissivity models

8. Figure 7. Impact of MODIS AMVs on the operational GFS forecast at 500hPa (60°N - 90°N). (10 Aug. – 23 Sept. 2004); the pink (dashed) curve shows the ACC with (without) MODIS AMVs

9. Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004- Assim1

10. Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

11. Figure 1(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

12. Figure1(a). 500hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern Hemisphere, January 2004

13. Impact of AIRS spatial data density/QC (Snow, SSI/eo/April 2005/nw)

14. The Challenge: With so many Satellites & Sensors, how to set priorities GRACE Aqua Cloudsat CALIPSO GIFTS TRMM SSMIS TOPEX NPP Landsat Meteor/ SAGE GOES-R COSMIC/GPS NOAA/POES NPOESS SeaWiFS Jason Aura Terra SORCE ICESat WindSAT

15. CHAMP to Raobs (Kuo) India Australia

16. CHAMP - ECMWF (Kuo) India Australia

17. Vertical temperature profile (Randel) tropical tropopause midlatitude tropopause

18. Extratropical temperature profiles often have multiple tropopauses (Randel) radiosonde at Charleston SC (lat 32 N)

19. statistical distribution of tropopause heights from radiosondes at Charleston 1950-2003

20. two examples from GPS data

21. statistics at Charleston from radiosonde and GPS data radiosondes GPS

22. Coverage of COSMIC GPS RO sounding in one day Green dots are COSMIC soundings. Red dots are radiosonde stations. GRAS will add ~15-20 percent

23. Short Term Priorities 05/06 • PREPARATIONS FOR METOP: -METOP/IASI -Complete Community RTM transmittance preparation for IASI - Upgrade Analysis for IASI -Assimilate synthetic IASI BUFR radiances in preparation for . METOP. - Complete preparations for HIRS, AMSU, MHS, ASCAT, GRAS, GOME-2, AVHRR) • SSMIS: Collaborate with the SSMIS CALVAL Team to jointly help assess SSMIS data. Accelerate assimilation into operational model as appropriate • GPSRO: GPS (CHAMP) assimilation and assessment. Prepare for (COSMIC) assimilation into operational model.

24. Outlook for CY 2005 • (1) QC • Test and implement additional QC checks (in lower troposphere, stratosphere, to account for superrefraction) • (2) Error • Better characterization of the refractivity (measurement) errors • Examine representativeness error. • Adjustment of the background error covariance matrix according to the results of the assimilation of profiles of refractivity. • (3) Experiments • Conduct a cycling experiment for one month period to assess the impact of the assimilation of the CHAMP RO profiles to get ready for COSMIC. • (4) Forward Operators • Implementation of the bending angle Forward Operator. (As the bending angle observations are less contaminated by the climatological guess field, a better performance in the assimilation system is expected. • (5) Pass CHAMP data in COSMIC format from CDAAC through OSDPD to JSCDA/NCEP for assimilation studies.