2. NWP – READINESS FOR THE NEXT GENERATION OF SATELLITE DATA John Le Marshall, JCSDA

3. Overview Background The JCSDA Mission, Vision Next Generation systems GOES-R, The instruments ABI, HES, SEISS, SIS, GLM Use of heritage instruments for risk reduction Future Summary

4. The Challenge Satellite Systems/Global Measurements Aqua Terra TRMM SORCE SeaWiFS Aura Meteor/ SAGE GRACE ICESat Cloudsat Jason CALIPSO GIFTS TOPEX Landsat NOAA/ POES GOES-R WindSAT NPP COSMIC/GPS SSMIS NPOESS

5. Draft Sample Only

6. Satellite Data used in NWP HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES, Meteosat, GMS winds GOES precipitation rate SSM/I precipitation rates TRMM precipitation rates SSM/I ocean surface wind speeds ERS-2 ocean surface wind vectors QuikScat ocean surface wind vectors AVHRR SST AVHRR vegetation fraction AVHRR surface type Multi-satellite snow cover Multi-satellite sea ice SBUV/2 ozone profile and total ozone Altimeter sea level observations (ocean data assimilation) AIRS radiances MODIS Winds…

7. Sounding data used operationally within the GMAO/NCEP Global Forecast System AIRS HIRS sounder radiances AMSU-A sounder radiances MSU AMSU-B sounder radiances GOES sounder radiances SBUV/2 ozone profile and total ozone On 14 - on 15 - off 16 - off 17 - on 15 - on 16 - on 17 - off 18 - on AQUA-on 14 - on 15 - on 16 - on 17 - on 10 - on (ch 1-15 ) 12 - on (ch 1-15 ) 16 - on 17 - on

8. NPOESS Satellite CMIS- μwave imager VIIRS- vis/IR imager CrIS- IR sounder ATMS- μwave sounder OMPS- ozone GPSOS- GPS occultation ADCS- data collection SESS- space environment APS- aerosol polarimeter SARSAT - search & rescue TSIS- solar irradiance ERBS- Earth radiation budget ALT- altimeter SS- survivability monitor CMIS VIIRS CrIS ATMS ERBS OMPS The NPOESS spacecraft has the requirement to operate in three different sun synchronous orbits, 1330, 2130 and 1730 with different configurations of fourteen different environmental sensors that provide environmental data records (EDRs) for space, ocean/water, land, radiation clouds and atmospheric parameters. In order to meet this requirement, the prime NPOESS contractor, Northrop Grumman Space Technology, is using their flight-qualified NPOESS T430 spacecraft. This spacecraft leverages extensive experience on NASA’s EOS Aqua and Aura programs that integrated similar sensors as NPOESS. As was required for EOS, the NPOESS T430 structure is an optically and dynamically stable platform specifically designed for earth observation missions with complex sensor suites. In order to manage engineering, design, and integration risks, a single spacecraft bus for all three orbits provides cost-effective support for accelerated launch call-up and operation requirement changes. In most cases, a sensor can be easily deployed in a different orbit because it will be placed in the same position on the any spacecraft. There are ample resource margins for the sensors, allowing for compensation due to changes in sensor requirements and future planned improvements. The spacecraft still has reserve mass and power margin for the most stressing 1330 orbit, which has eleven sensors. The five panel solar array, expandable to six, is one design, providing power in the different orbits and configurations. The NPOESS spacecraft has the requirement to operate in three different sun synchronous orbits, 1330, 2130 and 1730 with different configurations of fourteen different environmental sensors that provide environmental data records (EDRs) for space, ocean/water, land, radiation clouds and atmospheric parameters. In order to meet this requirement, the prime NPOESS contractor, Northrop Grumman Space Technology, is using their flight-qualified NPOESS T430 spacecraft. This spacecraft leverages extensive experience on NASA’s EOS Aqua and Aura programs that integrated similar sensors as NPOESS. As was required for EOS, the NPOESS T430 structure is an optically and dynamically stable platform specifically designed for earth observation missions with complex sensor suites. In order to manage engineering, design, and integration risks, a single spacecraft bus for all three orbits provides cost-effective support for accelerated launch call-up and operation requirement changes. In most cases, a sensor can be easily deployed in a different orbit because it will be placed in the same position on the any spacecraft. There are ample resource margins for the sensors, allowing for compensation due to changes in sensor requirements and future planned improvements. The spacecraft still has reserve mass and power margin for the most stressing 1330 orbit, which has eleven sensors. The five panel solar array, expandable to six, is one design, providing power in the different orbits and configurations.

9. 5-Order Magnitude Increase in s atellite Data Over 10 Years Count (Millions) Daily Upper Air Observation Count Year Satellite Instruments by Platform Count NPOESS METEOP NOAA Windsat GOES DMSP 19902010 Year

10. GOES - R ABI – Advanced Baseline Imager HES – Hyperspectral Environmental Suite SEISS – Space Environment In- Situ Suite including the Magnetospheric Particle Sensor (MPS); Energetic Heavy Ion Sensor (EHIS); Solar & Galactic Proton Sensor (SGPS) SIS – Solar Imaging Suite including the Solar X-Ray Imager (SXI); Solar X-Ray Sensor (SXS); Extreme Ultraviolet Sensor (EUVS) GLM – GEO Lightning Mapper

11. Advanced Baseline Imager (ABI) ABI BandWavelength Range (µm) Central Wavelength (µm) Sample Objective(s) 10.45-0.490.47Daytime aerosol-over-land, Color imagery 20.59-0.690.64Daytime clouds fog, insolation, winds 30.84-0.880.86Daytime vegetation & aerosol-over-water, winds 41.365-1.3951.38Daytime cirrus cloud 51.58-1.641.61Daytime cloud water, snow 62.235 - 2.2852.26Day land/cloud properties, particle size, vegetation 73.80-4.003.9Sfc. & cloud/fog at night, fire 85.77-6.66.19High-level atmospheric water vapor, winds, rainfall 96.75-7.156.95Mid-level atmospheric water vapor, winds, rainfall 10 7.24-7.447.34Lower-level water vapor, winds & SO2 118.3-8.78.5Total water for stability, cloud phase, dust, SO2 129.42-9.89.61Total ozone, turbulence, winds 1310.1-10.610.35Surface properties, low-level moisture & cloud 1410.8-11.611.2Total water for SST, clouds, rainfall 1613.0-13.613.3Air temp & cloud heights and amounts

12. Advanced Baseline Imager (ABI) ABI Requirements ABICurrent GOES Spatial Coverage Rate Full disk CONUS 4 per hour 12 per hour Every 3 hours ~ 4 per hour Spatial resolution 0.64 μm VIS Other VIS/ near IR Bands > 2 μm 0.5 km 1.0 km 2.0 km ~ 1 km Na ~ 4 km Spectral coverage16 bands5 bands Total radiances over 24 hours = 172, 500, 000, 000

13. Hyperspectral Environmental Suite (HES) Band HES Band NumberSpectral Range (um) Band Continuity LWIR115.38 - 8.33 (T)Contiguous MWIR (option 1)26.06 - 4.65 (T)Contiguous MWIR (option 2)28.26 - 5.74 (T), 8.26 - 4.65 (G)Contiguous SWIR34.65 - 4.44 (T), 4.65 - 3.68 (G)Contiguous VIS40.52 - 0.70 (T)Contiguous Reflected Solar < 1 um50.40 - 1.0 (T) Non-Contiguous / Contiguous 0.570 um50.565-0.575Non-Contiguous Reflected Solar > 1 um (option 1-CW)61.0 - 2.285 (G)Contiguous Reflected Solar > 1 um (option 2-CW)6 1.35-1.41, 1.55-1.67, 2.235- 2.285 (G)Non-contiguous LWIR for CW711.2 - 12.3 (G)Non-contiguous (T) = Threshold, denotes required coverage (G) = Goal, denotes coverage under study during formulation

14. Hyperspectral Environmental Suite (HES) HES Requirements HESCurrent GOES Coverage RateSounding disk/hrCONUS/hr Horizontal Resolution Sampling distance Individual sounding 10 km 30 – 50 km Vertical Resolution1 km3 km Accuracy Temperature Relative Humidity 1°K 10% 2°K 20% Total radiances over 24 hours = 93, 750, 000, 000

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

16. NASA/Goddard Global Modeling & Assimilation Office NOAA/NESDIS Office of Satellite Applications & Research NOAA/OAR Office of Weather and Air Quality NOAA/NCEP Environmental Modeling Center US Navy Oceanographer of the Navy, Office of Naval Research (NRL) US Air Force AF Director of Weather AF Weather Agency PARTNERS Joint Center for Satellite Data Assimilation

17. JCSDA Structure Associate Administrators NASA: Science NOAA: NESDIS, NWS, OAR DoD: Navy, Air Force Management Oversight Board of Directors: NOAA NWS: L. Uccellini (Chair) NASA GSFC: F. Einaudi NOAA NESDIS: A. Powell NOAA OAR: M. Uhart Navy: S. Chang USAF: J. Lanici/M. Farrar Advisory Panel Rotating Chair Science Steering Committee Joint Center for Satellite Data Assimilation Staff Director: J. Le Marshall Deputy Directors: S tephen Lord – NWS /NCEP James Yoe - NESDIS Lars Peter Riishogjaard – GSFC, GMAO Pat Phoebus – DoD,NRL Secretary: Ada Armstrong Consultant: George Ohring Technical Liaisons: NOAA/NWS/NCEP – J. Derber NASA/GMAO – M. Rienecker NOAA/OAR – A. Gasiewski NOAA/NESDIS – D. Tarpley Navy – N. Baker USAF – M. McATee Army – G. Mc Williams

18. JCSDA Mission and Vision Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather climate and environmental analysis and prediction models Vision: A weather, climate and environmental analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations and to effectively use the integrated observations of the GEOSS

19. 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

20. JCSDA SCIENCE PRIORITIES Science Priority I - Improve Radiative Transfer Models - Atmospheric Radiative Transfer Modeling – The Community Radiative Transfer Model (CRTM) - Surface Emissivity Modeling Science Priority II - Prepare for Advanced Operational Instruments Science Priority III -Assimilating Observations of Clouds and Precipitation - Assimilation of Precipitation - Direct Assimilation of Radiances in Cloudy and Precipitation Conditions Science Priority IV - Assimilation of Land Surface Observations from Satellites Science Priority V - Assimilation of Satellite Oceanic Observations Science Priority VI – Assimilation for air quality forecasts

21. JCSDA Major Accomplishments Include Common assimilation infrastructure at NOAA and NASA Community radiative transfer model V2 released Common NOAA/NASA land data assimilation system Interfaces between JCSDA models and external researchers Operational Implementations Include: Snow/sea ice emissivity model – permits 300% increase in sounding data usage over high latitudes – improved forecasts MODIS winds, polar regions, - improved forecasts AIRS radiances – improved forecasts New generation, physically based SST analysis - Improved SST Preparation for advanced satellite data such as METOP (IASI/AMSU/MHS), DMSP (SSMIS), COSMIC GPS data, EOS AMSR-E, GIFTS,GOES-R Impact studies of POES MHS, EOS AIRS/MODIS, Windsat, DMSP SSMIS……. on NWP through parallel experiments

22. NWP – READINESS FOR THE NEXT GENERATION OF SATELLITE DATA Some examples

23. Assimilation of GPS RO observations at JCSDA Lidia Cucurull, John Derber, Russ Treadon, Martin Lohmann, Jim Yeo…

24. GPS/COSMIC 24 transmitters 6 receivers3000 occultations/day

25. Information content from1D-Var studies IASI (Infrared Atmospheric Sounding Interferometer) RO (Radio Occultation) (Collard+Healy, QJRMS,2003)

26. GSI/GFS Impact studies: 2-month cycling at T62L64 n JCSDA has implemented and tested the capability of assimilating profiles of Refractivity (N)and soundings of Bending Angles (BA) in the GSI/GFS DA system. n Initial results are shown opposite.

27. USE OF SURFACE WIND VECTORS AT THE JCSDA J.Le Marshall

29. JCSDA WindSat Testing Coriolis/WindSat data is being used to assess the utility of passive polarimetric microwave radiometry in the production of sea surface winds for NWP Study accelerates NPOESS preparation and provides a chance to enhance the current global system Uses NCEP GDAS

30. JCSDA WindSat Testing Experiments –Control with no surface winds (Ops minus QuikSCAT) –Operational QuikSCAT only –WindSat only –QuikSCAT & WindSat winds Assessment underway

31. CRTM FASTEM AMSR-E radiance at low frequency contains signature on surface wind speed and temperature over Oceans. Surface emissivity plays an important role in direct radiance assimilation. The new emissivity model reduces the error in model radiance simulation. AMSR-E radiance assimilation in GSI

32. Aura/OMI Total Ozone Aura satellite launched in July 2004. NASA began providing OMI Total Ozone data to NOAA in NRT February 2006 OMI provides 1000x more obs than operationally assimilated SBUV/2. –~90,000 OMI obs/orbit vs. ~90 SBUV/2 obs/orbit OMI profile to become available soon. –same quality and vertical resolution as SBUV/2 but 1000x the number of profiles JCSDA is assimilating Aura/OMI total ozone into the NCEP GFS in test mode. Aura/HIRDLS profiles will be available for assimilation tests soon. –Profile is higher quality and higher resolution than SBUV/2

33. AMS 2006 - Future National Operational Environmental Satellites SymposiumRisk Reduction for NPOESS Using Heritage Sensors 33 NPOESS Satellite CMIS- μwave imager VIIRS- vis/IR imager CrIS- IR sounder ATMS- μwave sounder OMPS- ozone GPSOS- GPS occultation ADCS- data collection SESS- space environment APS- aerosol polarimeter SARSAT - search & rescue TSIS- solar irradiance ERBS- Earth radiation budget ALT- altimeter SS- survivability monitor The NPOESS spacecraft has the requirement to operate in three different sun synchronous orbits, 1330, 2130 and 1730 with different configurations of fourteen different environmental sensors that provide environmental data records (EDRs) for space, ocean/water, land, radiation clouds and atmospheric parameters. In order to meet this requirement, the prime NPOESS contractor, Northrop Grumman Space Technology, is using their flight-qualified NPOESS T430 spacecraft. This spacecraft leverages extensive experience on NASA’s EOS Aqua and Aura programs that integrated similar sensors as NPOESS. As was required for EOS, the NPOESS T430 structure is an optically and dynamically stable platform specifically designed for earth observation missions with complex sensor suites. In order to manage engineering, design, and integration risks, a single spacecraft bus for all three orbits provides cost-effective support for accelerated launch call-up and operation requirement changes. In most cases, a sensor can be easily deployed in a different orbit because it will be placed in the same position on the any spacecraft. There are ample resource margins for the sensors, allowing for compensation due to changes in sensor requirements and future planned improvements. The spacecraft still has reserve mass and power margin for the most stressing 1330 orbit, which has eleven sensors. The five panel solar array, expandable to six, is one design, providing power in the different orbits and configurations. The NPOESS spacecraft has the requirement to operate in three different sun synchronous orbits, 1330, 2130 and 1730 with different configurations of fourteen different environmental sensors that provide environmental data records (EDRs) for space, ocean/water, land, radiation clouds and atmospheric parameters. In order to meet this requirement, the prime NPOESS contractor, Northrop Grumman Space Technology, is using their flight-qualified NPOESS T430 spacecraft. This spacecraft leverages extensive experience on NASA’s EOS Aqua and Aura programs that integrated similar sensors as NPOESS. As was required for EOS, the NPOESS T430 structure is an optically and dynamically stable platform specifically designed for earth observation missions with complex sensor suites. In order to manage engineering, design, and integration risks, a single spacecraft bus for all three orbits provides cost-effective support for accelerated launch call-up and operation requirement changes. In most cases, a sensor can be easily deployed in a different orbit because it will be placed in the same position on the any spacecraft. There are ample resource margins for the sensors, allowing for compensation due to changes in sensor requirements and future planned improvements. The spacecraft still has reserve mass and power margin for the most stressing 1330 orbit, which has eleven sensors. The five panel solar array, expandable to six, is one design, providing power in the different orbits and configurations.

34. NPOESS/JCSDA The Instrument Complement: CrIS ATMS VIIRS CMIS OMPS GPSOS-deselected ALT The Heritage Instruments: AIRS, IASI MSU, AMSU, HSB, MHS AVHRR, MODIS SSMI, SSMIS, WINDSAT TOMS, SBUV CHAMP, SAC-C, COSMIC ALT

35. JCSDA is preparing to assimilate NPP data for operational use JCSDA is preparing to assimilate NPP data for operational use NPOESS Preparatory Project (NPP) The Instrument Complement: CrIS ATMS VIIRS OMPS

36. The Joint Center for Satellite Data Assimilation and GOES-R Risk Reduction Activity

37. GOES-R The Instrument Complement: ABI – Advanced Baseline Imager HES – Hyperspectral Environmental Suite SEISS – Space Environment In-Situ Suite including the Magnetospheric Particle Sensor (MPS); Energetic Heavy Ion Sensor (EHIS); Solar & Galactic Proton Sensor (SGPS) SIS – Solar Imaging Suite including the Solar X-Ray Imager (SXI); Solar X-Ray Sensor (SXS); Extreme Ultraviolet Sensor (EUVS) GLM – GEO Lightning Mapper The Heritage Instruments: GOES Imager, MODIS, AVHRR AIRS, IASI

38. JCSDA GOES-R RISK REDUCTION RELATED ACTIVITY Preparation for Data Assimilation GOES-R Instrument Radiative Transfer Modeling- Community Radiative Transfer Model (CRTM) Risk Reduction Instrument Studies, OSSEs –AIRS, … Data Assimilation Research/Risk Reduction using Heritage Instruments- AIRS, MODIS, HIRS, AVHRR, IASI, GOES … Participation in Calibration/Validation and preparation for early data access. Development of assimilation methodology for GOES-R data etc. (3D VAR, 4D VAR..) Preparation of the numerical forecast systems (GFS, WRF, HWRF ….) to use GOES-R data FY 2012 – Data Assimilation system prepared for use of GOES-R data

39. Using GOES Imager and MODIS data in Preparation for the Advanced Baseline Imager

40. Advanced Baseline Imager (ABI) ABI BandWavelength Range (µm)Central Wavelength (µm) Sample Objective(s) 10.45-0.490.47Daytime aerosol-over-land, Color imagery 20.59-0.690.64Daytime clouds fog, insolation, winds 30.84-0.880.86Daytime vegetation & aerosol-over-water, winds 41.365-1.3951.38Daytime cirrus cloud 51.58-1.641.61Daytime cloud water, snow 62.235 - 2.2852.26Day land/cloud properties, particle size, vegetation 73.80-4.003.9Sfc. & cloud/fog at night, fire 85.77-6.66.19High-level atmospheric water vapor, winds, rainfall 96.75-7.156.95Mid-level atmospheric water vapor, winds, rainfall 10 7.24-7.447.34Lower-level water vapor, winds & SO2 118.3-8.78.5Total water for stability, cloud phase, dust, SO2 129.42-9.89.61Total ozone, turbulence, winds 1310.1-10.610.35Surface properties, low-level moisture & cloud 1410.8-11.611.2Total water for SST, clouds, rainfall 1613.0-13.613.3Air temp & cloud heights and amounts

41. MODIS Wind Assimilation NCEP Global Forecast System 10 Aug - 23 Sept, 2004 John Le Marshall (JCSDA) James Jung (CIMSS) Tom Zapotocny (CIMSS) John Derber (NCEP) Jaime Daniels (NESDIS) Chris Redder (GMAO)

42. The Trial NESDIS generated AMVs –10 Aug - 23 Sept 2004 –Terra & Aqua satellites Middle image used for tracers Post NESDIS QC used, particularly for gross errors cf. background and for winds above tropopause Winds assimilated only in second last analysis (later “final” analysis) to simulate realistic data availability.

43. Global Forecast System Background Operational SSI Analysis used Operational GFS T254L64 with reductions in resolution at 84 (T170L42) and 180 (T126L28) hours. 2.5hr cut off

45. 13. 2 43. 6 66.594. 9 102. 8 157. 1 227. 9 301.1Cntrl 11.434. 8 60.482. 6 89.0135. 3 183. 0 252.0 Cntrl + MODIS 7468646152463934Cases (#) 00- h 12- h 24- h 36- h 48-h72-h96-h120- h Time AVERAGE HURRICANE TRACK ERRORS (NM) 2004 ATLANTIC BASIN Results compiled by Qing Fu Liu.

46. ERROR CHARACTERIZATION OF ATMOSPHERIC MOTION VECTORS AT THE JCSDA J.Le Marshall Picture

47. Expected Error - provides RMS Error (RMS) Estimated from five QI components wind speed vertical wind shear temperature shear pressure level which are used as predictands for root mean square error

49. Accuracy of EE GOES - EIR LOW EENRMS (m/s) 210261.35 327991.69 420392.07 59322.21 62312.53 7402.68

50. Using AIRS data in Preparation for the Hyperspectral Evironmental Sounder

51. 1-31 January 2004 Used operational Global Forecast System (GFS) as Control Used Enhanced Operational GFS system Plus AIRS as Experimental System AIRS Data Assimilation J. Le Marshall, J. Jung, J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W. Wolf and H-S Liu and J. Joiner

52. Satellite data used operationally within the NCEP Global Forecast System HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES 9,10,12, Meteosat atmospheric motion vectors GOES precipitation rate SSM/I ocean surface wind speeds SSM/I precipitation rates TRMM precipitation rates ERS-2 ocean surface wind vectors QuikScat ocean surface wind vectors AVHRR SST AVHRR vegetation fraction AVHRR surface type Multi-satellite snow cover Multi-satellite sea ice SBUV/2 ozone profile and total ozone

53. AIRS Data Usage per Six Hourly Analysis Cycle Data CategoryNumber of AIRS Channels Total Data Input to Analysis Data Selected for Possible Use Data Used in 3D VAR Analysis (Clear Radiances) ~200x10 6 radiances (channels) ~2.1x10 6 radiances (channels) ~0.85x10 6 radiances (channels)

54. AIRS data coverage at 06 UTC on 31 January 2004. (Obs-Calc. Brightness Temperatures at 661.8 cm -1 are shown)

55. 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS Data Northern Hemisphere, January 2004

56. 500hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS Data Southern hemisphere, January 2004

57. 500hPa Z Anomaly Correlations 5 Day Forecast for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, (1-27) January 2004

58. AIRS Data Assimilation MOISTURE Forecast Impact evaluates which forecast (with or without AIRS) is closer to the analysis valid at the same time. Impact = 100* [Err(Cntl) – Err(AIRS)]/Err(Cntl) Where the first term on the right is the error in the Cntl forecast. The second term is the error in the AIRS forecast. Dividing by the error in the control forecast and multiplying by 100 normalizes the results and provides a percent improvement/degradation. A positive Forecast Impact means the forecast is better with AIRS included. Error in AIRS fcst Error in control fcst Error in AIRS fcst Error in control fcst Error in AIRS fcst Error in control fcst Error in AIRS fcst Error in control fcst

59. Low Level Relative Humidity

60. 1-31 January 2004 Used operational GFS system as Control Used Enhanced Operational GFS system Plus AIRS as Experimental System First example of clear positive impact both N and S Hemispheres AIRS Data Assimilation

61. Hyperspectral Data Assimilation JCSDA is currently undertaking studies to document the effects of data spatial density, spectral coverage and the use of imager data on hyperspectral radiance assimilation

62. Hyperspectral Data Assimilation JCSDA is well prepared for assimilating HES hyperspectral radiance data.

63. The Joint Center for Satellite Data Assimilation GOES – R OSSE John Le Marshall, JCSDA

64. GOES – R OSSE Examined proposal to develop a HES based on short wave observations Used all available AIRS channels below 9.3μm to simulate such an instrument observations -115 of 281 channel set used Compared to long wave and shortwave instrument – All available (251 of 281) channels used. Asimilation used current operational practice.

65. GOES – R OSSE Radiative Transfer System: JCSDA CRTM Assim. System. NCEP Global Forecast System Operational SSI (3DVAR) version used Operational GFS T254 L64. 2.5hr data cut off Control op.data base includes AQUA AMSU-A NCEP verification scheme

66. The OSSE-Detail AIRS related weights/noise unmodified Used NCEP Operational verification scheme.

67. Satellite data used operationally within the NCEP Global Forecast System HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES 9,10,12, Meteosat atmospheric motion vectors GOES precipitation rate SSM/I ocean surface wind speeds SSM/I precipitation rates TRMM precipitation rates ERS-2 ocean surface wind vectors Quikscat ocean surface wind vectors AVHRR SST AVHRR vegetation fraction AVHRR surface type Multi-satellite snow cover Multi-satellite sea ice SBUV/2 ozone profile and total ozone Here AQUA AMSU-A included

69. Hyperspectral Data Assimilation (AIRS/IASI/HES) – The Next Steps – Fast Radiative Transfer Modelling (OSS, Superfast RTM) OSSEs : AIRS – SW/LW Comparison (GOES-R study) GFS Hyperspectral Assimilation studies using: full spatial resolution AIRS data with advanced surface Є. full spatial resolution AIRS/MODIS 1 cloud characterization Assim. full spatial resolution AIRS/MODIS 1 Sounding Channel Assim. full spatial res. AIRS with Cloud Cleared Radiances. 1 Proxy for ABI/HES

70. Surface Emissivity (ε) Estimation Methods Geographic Look Up Tables (LUTs) - (2) Regression based on theoretical estimates – (2) * Minimum Variance, provides T surf and ε * Eigenvector technique Variational Minimisation – goal * In use currently in experiments

71. IR HYPERSPECTRAL EMISSIVITY - ICE and SNOW Sample Max/Min Mean computed from synthetic radiance sample From Lihang Zhou Emissivity Wavenumber

73. Summary: Over the past three years the JCSDA has been developing a balanced program to support future operational data assimilation in NASA, NOAA and the DoD. Due deference to the science priority areas has facilitated this balance. The current and future satellite programs including GOES – R have been examined to develop a strategy to prepare for efficient implementation of satellite data as it becomes available.

74. Summary Cont‘d: A very important activity for the Center is planning in relation to the form of the next generation assimilation systems to be used by the partners. Current strategic planning and development involves the use of the 4D variational approach. In conclusion the GOES-R Program and Users will benefit from this activity which will enable the JCSDA Partners to use GOES-R data soon after launch.