JCSDA Briefing

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The Joint Center For Satellite Data
Assimilation (JCSDA) Helping to improve
Climate, Weather, Ocean and Air Quality Forecasts
and Improve The Quality of Climate Data Sets
John Le Marshall Director, JCSDA
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The Joint Center For Satellite Data Assimilation
(JCSDA) Helping to improve Climate, Weather,
Ocean and Air Quality Forecasts and Improve The
Quality of Climate Data Sets

• Australian Federal Departmental
• Excellence Award
• SPIE Award for Scientific
• Achievement in Remote Sensing
• NASA Exceptional Scientific Achievement Medal
• Current/Recent
• Co-Chair International TOVS
• Working Group
• Adjunct Professor University of
• New South Wales Mathematics
• Adjunct Professor La Trobe
• University Physics
• Adjunct Professor University of
• Wisconsin Meteorology and
• Oceanography

Dr. John F. Le Marshall Director, JCSDA NASA,
NOAA, DoD Joint Center for Satellite Data
Assimilation
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Overview

• JCSDA Background
• The Satellite Program The Challenge
• JCSDA
• NPOESS
• Preparation for NPOESS using Heritage Instruments
• Plans/Future Prospects
• Summary

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Background

• The value of satellite Observations

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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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Anomaly correlation for days 0 to 7 for 500 hPa
geopotential height in the zonal band 20-80
South for January/February. The red and blue
arrows indicate use of satellite data in the
forecast model has doubled the length of a
useful forecast ( ie a forecast with AC0.6).

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The Challenge

• 5-Order Magnitude Increase in Satellite Data
  Over 10 Years

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The Challenge Satellite Systems/Global
Measurements
GRACE
Aqua
Cloudsat
CALIPSO
TRMM
GIFTS
SSMIS
TOPEX
NPP
Landsat
MSG
Meteor/ SAGE
GOES-R
COSMIC/GPS
NOAA/POES
NPOESS
SeaWiFS
Aura
Jason
Terra
SORCE
ICESat
WindSAT
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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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JCSDA Instrument Database June 2006
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The JCSDA

• History, Mission, Vision, .

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History
In 2001 the Joint Center was established2 by
NASA and NOAA and in 2002, the JCSDA expanded its
partnerships to include the U.S. Navy and Air
Force weather agencies. 2 Joint Center for
Satellite Data Assimilation Luis Uccellini,
Franco Einaudi, James F. W. Purdom, David Rogers
April 2000.
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JCSDA Partners
Pending
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JCSDA Mission and Vision

• Mission Accelerate and improve the quantitative
  use of research and operational satellite data in
  weather. ocean, climate and environmental
  analysis and prediction models
• Vision A weather, ocean, 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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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

Strat. Plan/JTOPS
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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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Major Accomplishments

• Common assimilation infrastructure at NOAA and
  NASA
• Community radiative transfer model
• Common NOAA/NASA land data assimilation system
• Interfaces between JCSDA models and external
  researchers
• Snow/sea ice emissivity model permits 300
  increase in sounding data usage over high
  latitudes improved polar forecasts
• MODIS winds, polar regions, - improved forecasts
  - Implemented
• AIRS radiances assimilated improved forecasts -
  Implemented
• Improved physically based SST analysis -
  Implemented
• Preparation for advanced satellite data such as
  METOP (IASI,AMSU,MHS), , NPP (CrIS, ATMS.),
  NPOESS, GOES-R data underway.
• Advanced satellite data systems such as DMSP
  (SSMIS), CHAMP GPS, COSMIC GPS, WindSat tested
  for implementation.
• Impact studies of POES AMSU, HIRS, EOS
  AIRS/MODIS, DMSP SSMIS, WindSat, CHAMP GPS on NWP
  through EMC parallel experiments active
• Data denial experiments completed for major data
  base components in support of system optimisation
  
• OSSE studies completed
• Strategic plans of all Partners include 4D-VAR

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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
• MODIS Winds

31 instruments
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Sounding data used operationally within the
GMAO/NCEP Global Forecast System
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Some Satellite Data in the Process of Being
Transitioned into Operations
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NPOESS

• The Instruments
• Applications
• -NWP/Environmental Analysis and Prediction
• -Ocean Analysis and Prediction
• -Climate Short/Long Term Prediction,
  Reanalyses (tuning, calibration)
• Preparations for NPOESS The CRTM
• Preparations for NPOESS- Assimilating Data from
  Heritage Instruments

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NPOESS

• The Instruments
• Applications
• -NWP/Environmental Analysis and Prediction
• -Ocean Analysis and Prediction
• -Climate Short/Long Term Prediction,
  Reanalyses (tuning, calibration)

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NPOESS Instrument Summary-2005
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NPOESS Instrument Summary-2006
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NPOESS provides key climate information on
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NPOESS provides key environmental analysis and
prediction information on
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NPOESS

• Applications
• -NWP/Environmental Analysis and Prediction
• -Ocean Analysis and Prediction
• -Climate Short/Long Term Prediction,
  Reanalyses (tuning, calibration)
• Note
• Multi-Variate Multi-Instrument Analysis
• (Radiance) Observations Tuned/Cross Calibrated
• -Differences due to Radiative Transfer Error
  , Model Error (bias), Observation Error,
  Calibration Error, .
• Modern Analysis Methods Aid Use Of Asynoptic
  Observations

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JCSDA NPOESSPreparation

• Preparations for NPOESS The CRTM
• Preparations for NPOESS- Assimilating Data from
  Heritage Instruments

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PREPARATION FOR NPOESSDevelopment and
Implementation of the Community Radiative
Transfer Model (CRTM)
P. van Delst, Q. Liu, F. Weng, Y. Chen, D. Groff,
B. Yan, N. Nalli, R. Treadon, J. Derber and Y.
Han ..
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Community Contributions

• Community Research Radiative Transfer science
• AER. Inc Optimal Spectral Sampling (OSS) Method
• NRL Improving Microwave Emissivity Model (MEM)
  in deserts
• NOAA/ETL Fully polarmetric surface models and
  microwave radiative transfer model
• UCLA Delta 4 stream vector radiative transfer
  model
• UMBC aerosol scattering
• UWisc Successive Order of Iteration
• CIRA/CU SHDOMPPDA
• UMBC SARTA
• Princeton Univ snow emissivity model
  improvement
• NESDIS/ORA Snow, sea ice, microwave land
  emissivity models, vector discrete ordinate
  radiative transfer (VDISORT), advanced
  double/adding (ADA), ocean polarimetric,
  scattering models for all wavelengths
• Core team (JCSDA - STAR/EMC) Smooth transition
  from research to operations
• Maintenance of CRTM (OPTRAN/OSS coeffs.,
  Emissivity upgrade)
• CRTM interface
• Benchmark tests for model selection
• Integration of new science into CRTM

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Major Progress

• CRTM has been integrated into the GSI at NCEP/EMC
  
• Beta version CRTM has been released to the public
• CRTM with OSS (Optimal Spectral Sampling) has
  been preliminarily implemented and is being
  evaluated and improved.

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COMMUNITY RADIATIVE TRANSFER MODEL CRTM
Below are some of the instruments for which we
currently have transmittance coefficients.
abi_gr (gr GOES-R) airs_aqua amsre_aqua
amsua_aqua amsua_n15 amsua_n16 amsua_n17
amsua_n18 amsub_n15 amsub_n16 amsub_n17
avhrr2_n10 avhrr2_n11 avhrr2_n12 avhrr2_n14
avhrr3_n15 avhrr3_n16 avhrr3_n17 avhrr3_n18
hirs2_n10 hirs2_n11 hirs2_n12 hirs2_n14 hirs3_n15
hirs3_n16 hirs3_n17 hirs3_n18 hsb_aqua imgr_g08
imgr_g09 imgr_g10 imgr_g11 imgr_g12 mhs_n18
modisD01_aqua (D01 detector 1, D02 detector
2, etc) modisD01_terra modisD02_aqua
modisD02_terra modisD03_aqua modisD03_terra
modisD04_aqua modisD04_terra modisD05_aqua
modisD05_terra modisD06_aqua modisD06_terra
modisD07_aqua modisD07_terra modisD08_aqua
modisD08_terra modisD09_aqua modisD09_terra
modisD10_aqua modisD10_terra modis_aqua (detector
average) modis_terra (detector average) msu_n14
sndr_g08 sndr_g09 sndr_g10 sndr_g11 sndr_g12
ssmi_f13 ssmi_f14 ssmi_f15 ssmis_f16 ssmt2_f14
vissrDetA_gms5 windsat_coriolis..
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Preparation for NPOESS Using
Heritage Instruments Some Recent
Advances
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NPOESS/JCSDA

• The Heritage Instruments
• AIRS, IASI
• MSU, AMSU, HSB, MHS
• AVHRR, MODIS
• TOMS, SBUV
• SSMI, SSMIS, WINDSAT
• CHAMP, SAC-C, COSMIC

• The NPOESS Instruments
• CrIS
• ATMS
• VIIRS
• OMPS
• CMIS
• GPSOS-demanifest

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NPOESS Instruments CrIS, ATMS (CrIMSS)
Cross-track Infrared and Advanced Technology
Microwave Sounders
CrIMSS will operationally produce high vertical
resolution profile measurements of temperature,
water vapor, and pressure. CrIMSS Mission
Products Primary Temperature
profiles Moisture profiles Pressure
profiles Calibrated radiances surface
temperature Secondary Total ozone Sea Cloud
top parameters Precipitable water ERB
products In addition to providing operational
temperature, moisture, and pressure profiles,
CrIMSS has the potential to provide other surface
and atmo- spheric science data, including total
ozone and sea surface temperature.
CrIS Instrument Characteristics Spectral
Range LWIR Band 650-1095 cm-1 MWIR
Band 1210-1750 cm-1 SWIR Band 2155-2550
cm-1 Spectral Resolution LWIR Band lt0.625cm-1 MWI
R Band lt1.25cm-1 SWIR Band lt2.50cm-1 Registration
Band-to-Band Co-registration lt1.4 FOV
Jitter 71 urad/axis Mapping accuracy lt1.5
km Field-of-Regard (FOR) of FOV 3X3 FOV
Diameter (round) 14 km FOV shape match
0.014 degrees Stability Radiometric
lt.45LW,lt.58MW,lt.77SW Spectral ILS lt1 of
FWHM Spectral shift errors lt5 ppm Mass 152
kg Power 123 W Data rate 1.5 mbps Size
898x946x716 mm

50 Cross track Scans
ATMS Instrument
ATMS Instrument Characteristics
AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
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Using AIRS data in Preparation for the
Cross-track Infrared 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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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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Forecast Improvement 24-hr. Fcst 925hPa RH
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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 significant 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 and
spectral coverage on hyperspectral radiance
assimilation
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Hyperspectral Data Assimilation JCSDA is well
prepared for assimilating CrIS hyperspectral
radiance data.
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Using AVHRR and MODIS data In Preparation for
the VIIRS
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NPOESS Instruments VIIRS Visible/Infrared
Imager/Radiometer Suite
VIIRS Key Characteristics and Performance
Heritage and Risk Reduction
POES
-
Advanced Very High Resolution Radiometer
(AVHRR/3)
DMSP
-
Operational Linescan System (OLS)
Imaging Optics 18.4 cm Aperture, 114 cm Focal
Length
EOS
-
Moderate Resolution Imaging Spectroradiometer
Band
-
to
-
Band Registration (All Bands, Entire Scan)
(MODIS)
gt 80 per Axis
NPP
-
Early validation of operational instrument and
Orbital Average Power 240 Watts
algorithms
Mass 275 Kg
VIIRS Sensor Bands
The Visible/Infrared Imager/Radiometer Suite
collects visible/infrared imagery and radiometric
data. Data types include atmospheric, clouds,
earth radiation budget, clear-air land/water
surfaces, sea surface temperature, ocean color,
and low light visible imagery. Primary instrument
for satisfying 27 environmental data records
(EDRs).
AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
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Improved NCEP SST AnalysisXu Li, John
DerberEMC/NCEP
Project Objectives To Improve SST
Analysis Use satellite data more
effectively Resolve diurnal variation Improve
first guess
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JCSDA has developed an operational physical SST
estimation algorithm suitable for use with VIIRS
data
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MODIS Wind Assimilation into the GMAO/NCEP
Global Forecast System
John Le Marshall (JCSDA) James Jung (CIMSS) Tom
Zapotocny (CIMSS) John Derber (NCEP) Jaime
Daniels (NESDIS)
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AMV ESTIMATION 11µm and 6.7 µm gradient features
tracked Tracers selected in middle
image Histogram, H2O intercept method, forecast
model and auto editor used for height assignment
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Water Vapor Winds
Low Level Mid Level High Level
05 March 2001 Daily composite of 6.7 micron
MODIS data over half of the Arctic region. Winds
were derived over a period of 12 hours. There are
about 13,000 vectors in the image. Vector colors
indicate pressure level - yellow below 700 hPa,
cyan 400-700 hPa, purple above 400 hPa.
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Global Forecast System Background

• Operational SSI (3DVAR) version used
• Operational GFS T254L64 with reductions in
  resolution at 84 (T170L42) and 180 (T126L28)
  hours. 2.5hr cut off
• Winds assimilated only in second last analysis
  (later final analysis) to simulate realistic
  data availability.

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Satellite data used operationally within the
GMAO/NCEP Global Forecast System
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Results Northern Hemisphere / The Arctic
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Southern Hemisphere / Antarctica
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2004 ATLANTIC BASIN
AVERAGE HURRICANE TRACK ERRORS (NM)
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Discussion and Conclusions

• Overall positive impact
• Post NESDIS QC used, particularly for gross
  errors cf.
• background and for winds above tropopause
• Implemented in JCSDA Partner Organisations
• JCSDA is ready for VIIRS polar wind
  assimilation

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MODIS WINDS v2. Improved winds/QC use of EE
AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
64
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Polarimetric Radiative Transfer Using SSMI,
SSMIS, WindSat AMSR(E) data in Preparation for a
Scanning Imager/Sounder
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SSMIS Radiance Assimilation
NCEP Global Forecast System 10 August - 10
September 2005 NCEP GFS Valid September
2006
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SSM/IS Radiance Assimilation in GSI
Period00z 10 Aug.-00z 10Sep. 2006
Assimilation System GSI 3D-Var Forecast
model NCEP Operational global model
(Sep.2006) Resolution T382L64 Data EXPC
Operational EXPS Operational UKMO SSMIS data
(removed flagged
data)
Preliminary Results Improved A.C. 500 hPa
height. Requires further investigation of data
quality
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NCEP AMSR-E Radiance Assimilation
Period 4-week cycling (Aug. 12, 2005 - Sept.
11, 2005) System Analysis GSI ( May. 2006
release version) New MW Ocean
emissivity model Forecast Operational
forecast model Resolution
T382L64 Data set Cntl same as operational
Test Cntl AMSR-E radiance data
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AMSR-E Radiance Assimilation in GSI
Period00z 12 Aug.-00z 11 Sep. 2005
Assimilation System GSI 3D-Var Forecast
model NCEP Operational global model
(May.2006) Resolution T382L64 Data Control
Operational Test1 Operational AMSR-E
(FASTEM1) Test2 Operational AMSR-E (New EM)
Control
Test1
Test2
Preliminary Results Improved A.C. 500 hPa
height. Decrease of RMS of surface wind speed
analysis increment
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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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WindSat v Ops - QuikSCAT
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Windsat Impact on operations
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Windsat Impact on operations
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Assimilation of GPS RO observations at JCSDA

• Lidia Cucurull, John Derber, Russ Treadon, Jim
  Yoe

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Using COSMIC GPS Data in Preparation for the
GPSOS
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GPS RO / COSMIC
3000 occultations/day
6 receivers
24 transmitters
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GPS RO /COSMIC

• The COnstellation of Satellites for
• Meteorology, Ionosphere, and Climate
• A Multinational Program
• Taiwan and the United States of America
• A Multi-agency Effort
• NSPO (Taiwan), NSF, UCAR,
• NOAA, NASA, USAF
• Based on the GPS Radio Occultation Method

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GPS RO / COSMIC

• Goals are to provide
• Limb soundings with high vertical resolution
• All-weather operating capability
• Measurements of Doppler delay based on
  temperature and humidity variations, convertible
  to bending angle, refractivity, and higher order
  products (i.e., temperature/humidity)
• Suitable for direct assimilation in NWP models
• Self-calibrated soundings at low cost for climate
  benchmark

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Information content from1D-Var studiesIASI
(Infrared Atmospheric Sounding Interferometer)RO
(Radio Occultation) - METOP
(Collard Healy, QJRMS,2003)
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GPS RO / COSMIC (contd)

• COSMIC launched April 2006
• Lifetime 5 years
• Operations funded through March 08

COSMIC data to be assimilated operationally
after move to new GSI analysis
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Early impact experiments (T382) with COSMIC

• Anomaly correlation as a function of forecast
  day for two different experiments
• E (assimilation of operational obs),
• BND (E COSMIC bending angle).
• Only COSMIC observations available in
  operations have been used in BND.
• Only COSMIC observations lt 30 km
• These results might have been impacted by the
  development stage of the GSI system

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Fit to Radiosondes November 2006

• Solid lines CTL
• Dashed lines CTL bending angle

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Summary

• NPOESS will provide higher spatial and spectral
  resolution data for environmental and climate
  applications
• These data, used with modern data assimilation
  methods, will lead to significantly improved
  weather, ocean, climate and air quality
  forecasts.
• Experience has shown for early data exploitation
  it is vital JCSDA is involved in CAL/VAL activity
  and early data evaluation.
• Community RTM and emissivity model being expanded
  to include NPP then NPOESS instruments.
• Risk Reduction/OSSE Studies have been undertaken
  in support of NPOESS
• Work on AIRS, AMSU, AVHRR and MODIS assimilation
  as a prelude to using CrIS, ATMS and VIIRS on
  NPOESS is ongoing.
• Assimilation of GPS (CHAMP/COSMIC/GPSOS/GRAS)
  well advanced and will improve upper troposphere
  reanalyses.

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Summary

• Preparation for a polarimetric scanner/imager
  well underway using SSMI, SSMIS, AMSR(E) and
  WINDSAT observations.
• OMPS will be added to O3 sensing suite
• Modern data assimilation methods will aid in
  calibration/cross calibration, QC, climate
  analysis (use asynoptic obs),
• Some important environmental/climate
  observations still require attention
• Aerosols
• Solar irradiance
• Sea level
• Earth radiation budget

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Closing Remarks

• The next decade of the meteorological
  (multipurpose) satellite program promises to be
  as exciting as the first, given the opportunities
  provided by new observations, modern data
  assimilation techniques, improved environmental
  modeling capacity and burgeoning computer power.
• NPOESS will provide essential observations for
  improved environmental (ocean, atmosphere,
  climate) modeling and for improved climate data
  sets
• JCSDA will be well positioned to exploit the
  NPOESS component of the GEOSS in terms of
• Assimilation science
• Modeling science.
• Computing power

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The business of looking down is looking up
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