JCSDA Briefing

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

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

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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

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Sounding data used operationally within the
GMAO/NCEP Global Forecast System
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NPOESS Satellite
CMIS
ATMS
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
VIIRS
CrIS
OMPS
ERBS
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 NASAs 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.
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5-Order Magnitude Increase in satellite Data
Over 10 Years

Satellite Instruments by Platform
Daily Upper Air Observation Count
NPOESS METEOP NOAA Windsat GOES DMSP
Count
Count (Millions)
1990
2010
Year
Year
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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
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Advanced Baseline Imager (ABI)
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Advanced Baseline Imager (ABI)
Total radiances over 24 hours 172, 500, 000, 000

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Hyperspectral Environmental Suite (HES)
(T) Threshold, denotes required coverage (G)
Goal, denotes coverage under study during
formulation


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Hyperspectral Environmental Suite (HES)
Total radiances over 24 hours 93, 750, 000, 000
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Data Assimilation Impacts in the NCEP GDAS
AMSU and All Conventional data provide nearly
the same amount of improvement to the Northern
Hemisphere.
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Joint Center for Satellite Data Assimilation
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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
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
Joint Center for Satellite Data Assimilation
Staff Director J. Le Marshall Deputy
Directors Stephen Lord NWS /NCEP James Yoe -
NESDIS Lars Peter Riishogjaard
GSFC, GMAO Pat Phoebus
DoD,NRL Secretary Ada Armstrong Consultant
George Ohring
Science Steering Committee
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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

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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

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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

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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

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NWP READINESS FOR THE NEXT GENERATION OF
SATELLITE DATA Some examples
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Assimilation of GPS RO observations at JCSDA

• Lidia Cucurull, John Derber, Russ Treadon, Martin
  Lohmann, Jim Yeo

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GPS/COSMIC
6 receivers
3000 occultations/day
24 transmitters
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Information content from1D-Var studiesIASI
(Infrared Atmospheric Sounding Interferometer)RO
(Radio Occultation)
(CollardHealy, QJRMS,2003)
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GSI/GFS Impact studies 2-month cycling at
T62L64

• 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.
• Initial results are shown opposite.

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

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JCSDA WindSat Testing

• Experiments
• Control with no surface winds (Ops minus
  QuikSCAT)
• Operational QuikSCAT only
• WindSat only
• QuikSCAT WindSat winds
• Assessment underway

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AMSR-E radiance assimilation in GSI
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.
CRTM
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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

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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 NASAs 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.
AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
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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

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NPOESS Preparatory Project (NPP)

• The Instrument Complement
• CrIS
• ATMS
• VIIRS
• OMPS

JCSDA is preparing to assimilate NPP data for
operational use
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The Joint Center for Satellite Data Assimilation
and GOES-R Risk Reduction
Activity
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GOES-R

• The Heritage Instruments
• GOES Imager, MODIS, AVHRR
• AIRS, IASI

• 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

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JCSDAGOES-R RISK REDUCTIONRELATED 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

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Using GOES Imager and MODIS data in Preparation
for the Advanced Baseline
Imager
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Advanced Baseline Imager (ABI)
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MODIS Wind Assimilation NCEP Global Forecast
System10 Aug - 23 Sept, 2004

• John Le Marshall (JCSDA)
• James Jung (CIMSS)
• Tom Zapotocny (CIMSS)
• John Derber (NCEP)
• Jaime Daniels (NESDIS)
• Chris Redder (GMAO)

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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.

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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

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2004 ATLANTIC BASIN
AVERAGE HURRICANE TRACK ERRORS (NM)
Results compiled by Qing Fu Liu.
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ERROR CHARACTERIZATION OF ATMOSPHERIC MOTION
VECTORS AT THE JCSDA
Picture
J.Le Marshall
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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
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Accuracy of EE
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Using AIRS data in Preparation for
the Hyperspectral Evironmental
Sounder
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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

• 1-31 January 2004
• Used operational Global Forecast System
  (GFS)
• as Control
• Used Enhanced Operational GFS system Plus
  AIRS
• as Experimental System

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Satellite data used operationally within the
NCEP Global Forecast System
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AIRS Data Usage per Six Hourly Analysis Cycle
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AIRS data coverage at 06 UTC on 31 January 2004.
(Obs-Calc. Brightness Temperatures at 661.8
cm-1are shown)
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500hPa Z Anomaly Correlations for the GFS with
(Ops.AIRS) and without (Ops.) AIRS Data Northern
Hemisphere, January 2004
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500hPa Anomaly Correlations for the GFS with
(Ops.AIRS) and without (Ops.) AIRS Data Southern
hemisphere, January 2004
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500hPa Z Anomaly Correlations 5 Day Forecast for
the GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Southern hemisphere, (1-27) January 2004
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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.
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AIRS Data Assimilation

• 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

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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
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Hyperspectral Data Assimilation JCSDA is well
prepared for assimilating HES hyperspectral
radiance data.
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The Joint Center for Satellite Data
Assimilation GOES R OSSE
John Le Marshall,
JCSDA
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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.

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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

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The OSSE-Detail

• AIRS related weights/noise unmodified
• Used NCEP Operational verification scheme.

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Satellite data used operationally within the
NCEP Global Forecast System
Here AQUA AMSU-A included
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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 ?. full spatial
resolution AIRS/MODIS1 cloud characterization
Assim. full spatial resolution AIRS/MODIS1
Sounding Channel Assim. full spatial res. AIRS
with Cloud Cleared Radiances.
1
Proxy for ABI/HES
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Surface Emissivity (e) Estimation Methods

• Geographic Look Up Tables (LUTs) - (2)
• Regression based on theoretical estimates (2)
  
• Minimum Variance, provides Tsurf and e
• Eigenvector technique
• Variational Minimisation goal
• In use currently in experiments

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