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

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

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

Draft Sample Only

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…

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

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.

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 Year

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

Advanced Baseline Imager (ABI) ABI BandWavelength Range (µm) Central Wavelength (µm) Sample Objective(s) Daytime aerosol-over-land, Color imagery Daytime clouds fog, insolation, winds Daytime vegetation & aerosol-over-water, winds Daytime cirrus cloud Daytime cloud water, snow Day land/cloud properties, particle size, vegetation Sfc. & cloud/fog at night, fire High-level atmospheric water vapor, winds, rainfall Mid-level atmospheric water vapor, winds, rainfall Lower-level water vapor, winds & SO Total water for stability, cloud phase, dust, SO Total ozone, turbulence, winds Surface properties, low-level moisture & cloud Total water for SST, clouds, rainfall Air temp & cloud heights and amounts

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

Hyperspectral Environmental Suite (HES) Band HES Band NumberSpectral Range (um) Band Continuity LWIR (T)Contiguous MWIR (option 1) (T)Contiguous MWIR (option 2) (T), (G)Contiguous SWIR (T), (G)Contiguous VIS (T)Contiguous Reflected Solar < 1 um (T) Non-Contiguous / Contiguous um Non-Contiguous Reflected Solar > 1 um (option 1-CW) (G)Contiguous Reflected Solar > 1 um (option 2-CW) , , (G)Non-contiguous LWIR for CW (G)Non-contiguous (T) = Threshold, denotes required coverage (G) = Goal, denotes coverage under study during formulation

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

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

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

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

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

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

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

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

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

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

GPS/COSMIC 24 transmitters 6 receivers3000 occultations/day

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

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.

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

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

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

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

Aura/OMI Total Ozone Aura satellite launched in July 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

AMS 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.

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

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

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

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

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

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

Advanced Baseline Imager (ABI) ABI BandWavelength Range (µm)Central Wavelength (µm) Sample Objective(s) Daytime aerosol-over-land, Color imagery Daytime clouds fog, insolation, winds Daytime vegetation & aerosol-over-water, winds Daytime cirrus cloud Daytime cloud water, snow Day land/cloud properties, particle size, vegetation Sfc. & cloud/fog at night, fire High-level atmospheric water vapor, winds, rainfall Mid-level atmospheric water vapor, winds, rainfall Lower-level water vapor, winds & SO Total water for stability, cloud phase, dust, SO Total ozone, turbulence, winds Surface properties, low-level moisture & cloud Total water for SST, clouds, rainfall Air temp & cloud heights and amounts

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)

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.

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

Cntrl Cntrl + MODIS Cases (#) 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.

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

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

Accuracy of EE GOES - EIR LOW EENRMS (m/s)

Using AIRS data in Preparation for the Hyperspectral Evironmental Sounder

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

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

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)

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

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

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

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

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

Low Level Relative Humidity

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

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

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

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

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.

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

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

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

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

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

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

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.

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.