JCSDA Briefing

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CALCULATING SEA SURFACE TEMPERATURE, EMISSIVITY
AND ATMOSPHERIC STATE USING HYPERSPECTRAL
RADIANCES J. Le Marshall, J. Jung, W. L. Smith,
E. Maturi, J. Derber, Xu Li, R. Treadon, S.
Lord, M. Goldberg and W. Wolf
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Overview

• JCSDA Background/Challenge/SST activity
• Hyperspectral Data Assimilation
• Hyperspectral emissivity/SST
• Plans/Future Prospects
• Summary

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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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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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Satellite Data used in NWP

• 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

• 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

gt32 instruments
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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 14 - on 15 - on 16 - on 17 - on 10 - on 12 - on 16 - on 17 - on
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CURRENT SATELLITE DATA - STATUS
AIRS v1. Implemented
AIRS v2. Completed Operational Trial - NCO
MODIS Winds Implemented
NOAA-18 AMSU-A Implemented
NOAA-18 MHS Completed Operational Trial - NCO
NOAA-17 SBUV Total Ozone Implemented
NOAA-17 SBUV Ozone Profile Implemented
SSM/I Radiances GSI impl. ( prod. Used in SSI)
COSMIC/CHAMP RT Assim. in GSI
SSMIS RT Assim. in GSI
MODIS Winds v2. RT Testing
WINDSAT RT Assim in GSI
AMSR/E Radiance Assimilation RT Assim IN GSI
AIRS/MODIS Sounding Channels Assim. ASSIM. Trial
GOES VIS and SW Winds To be Tested
GOES Hourly Winds To be Tested
GOES 11 and 12 Clear Sky Rad. Assim(6.7µm) To be Tested
MTSAT 1R Wind Assim. Assim Testing
AURA OMI Assim trial
TOPEX,JASON1,ERS-2 ENVISAT ALTIMETER Test and Development, Ops 06 GODAS
FY 2C CDW testing Underway

9 new instruments
Note ADM OSSEs Completed
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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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Hyperspectral/AIRS based SSTs
William L. Smith, R.O. Knuteson, H.E. Revercomb,
W. Feltz, H. B. Howell, W. P. Menzel, N. R.
Nalli, Otis Brown, Peter Minnett and Walter
McKeown. 1996 Observations of the Infrared
Radiative Properties of the Ocean Implications
for the Measurement of Sea Surface Temperature
via Satellite Remote Sensing. Bull. Amer. Meteor.
Soc. 77, 41 51. Nalli, N.R., 1995. Sea surface
skin temperature retrieval using the high
resolution interferimeter sounder (HIS). M.S.
Thesis, Dept. of Atmospheric and Oceanic
Sciences, University of Wisconsin Madison, 117
pp. . George Aumann et al. 2006 . . . . . . .
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USE OF AIRS HYPERSPECTRAL RADIANCES
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Development and Implementation Progress of
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 - ORA/EMC) Smooth transition
  from research to operation
• Maintenance of CRTM (OPTRAN/OSS coeff.,
  Emissivity upgrade)
• CRTM interface
• Benchmark tests for model selection
• Integration of new science into CRTM

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Progress

• CRTM v.0 used in NCEP SSI
• CRTM v.1 has been integrated into the GSI at
  NCEP/EMC (Dec. 2005)
• Beta version CRTM has been released to the public
• CRTM with OSS (Optimal Spectral Sampling) has
  been established 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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IMPROVED COMMUNITY RADIATIVE TRANSFER MODEL
CRTM
OPTRAN-V7 vs. OSS for AIRS channels
OSS
OPTRAN
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AQUA
Hyperspectral Data Assimilation
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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, J. Joiner, and J
Woollen 1 January 2004 31 January 2004 Used
operational GFS system as Control Used
Operational GFS system Plus AIRS as Experimental
System
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Table 1 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
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Improved NCEP SST AnalysisXu Li, John
DerberEMC/NCEP

• Progress
• SST physical retrieval code has been merged into
  GSI and provided to NCEP marine branch for
  operational use
• An extensive diagnostic study on the diurnal
  variation signals in in situ and satellite
  observations, SST retrievals, SST analysis and
  associated air-sea fluxes (NCEP GFS product)
  shows the SST diurnal variation needs to be
  addressed to improve the SST analysis product.
• 7-day 6-hourly SST analysis has been produced
  with GSI, after a new analysis variable, in situ
  and AVHRR data were introduced into GSI.
• Plan
• Analyze SST by assimilating satellite radiances
  directly with GSI
• Active ocean in the GFS
• Aerosol effects

AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
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Physical/Variational SST Retrieval
Formulation Cost Function
is brightness temperature (radiance), skin
temperature, atmospheric temperature vertical
profile and atmospheric water vapor vertical
profile respectively. is calculated with
radiative transfer model. is the
sensitivity of to
respectively. Initially, the and are
assumed not varying with height (z).
Therefore, The sum of these sensitivities with
height is used in the scheme for AVHRR data.
Upper-subscription represents analysis, first
guess and observation respectively.
Lower-subscription means the channel
index. is the error variance of and
respectively The solutions of are
solved by minimizing cost function J
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Improved NCEP SST AnalysisXu Li, John
DerberEMC/NCEP
AMS 2006 - Future National Operational
Environmental Satellites Symposium
Risk Reduction for NPOESS Using Heritage Sensors
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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

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The Trial

• Used full AIRS data stream used (JPL)
• NESDIS (ORA) generated BUFR files
• All FOVs, 324(281) channels
• 1 Jan 15 Feb 04
• Similar assimilation methodology to that used for
  operations
• Operational data cut-offs used
• Additional cloud handling added to 3D Var.
• Data thinning to ensure satisfying operational
  time constraints

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The Trial

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

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AIRS Assimilation

• Used 251 Out of 281 Channels
• - 73 - 86 Removed (Channels peak too High)
• - 1937 - 2109 Removed (Non LTE)
• - 2357 Removed (Large Obs Background Diff.)
• Used Shortwave at Night
• Wavenumber gt 2000 cm-1 Downweighted
• Wavenumber gt 2400cm-1 Removed

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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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Figure 5.Spectral locations for 324 AIRS selected
channel data distributed to NWP centers.
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Table 2 AIRS Data Usage per Six Hourly Analysis
Cycle
Data Category Number of AIRS Channels
Total Data Input to Analysis Data Selected for Possible Use Data Used in 3D VAR Analysis(Clear Radiances) 200x106 radiances (channels) 2.1x106 radiances (channels) 0.85x106 radiances (channels)
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Figure1(a). 1000hPa Anomaly Correlations for the
GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Southern hemisphere, January 2004
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Figure 1(b). 500hPa Z Anomaly Correlations for
the GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Southern hemisphere, January 2004
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Figure 2. 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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Figure3(a). 1000hPa Anomaly Correlations for the
GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Northern hemisphere, January 2004
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Figure 3(b). 500hPa Z Anomaly Correlations for
the GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Northern hemisphere, January 2004
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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, J. Joiner and J
Woollen January 2004 Used operational GFS
system as Control Used Operational GFS system
Plus AIRS as Experimental System Clear Positive
Impact Both Hemispheres.Implemented -2005
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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 Impact of Data
density... 10 August 20 September 2004
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AIRS Data Assimilation Impact of Spectral
density... 10 August 20 September 2004
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AIRS Data Assimilation AIRS in the GSI... 1
January 15 February 2004
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AIRS GSI, v2, GSI Contral 1/18 fovs v all
fov AIRS
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AIRS Data Assimilation Application of AIRS
Radiances over land ,water and ice

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Surface Emissivity (e) Estimation Methods

• IRSSE Model
• Geographic Look Up Tables (LUTs) - CRTM
• Regression based on theoretical estimates
• Minimum Variance, provides Tsurf and e
• Eigenvector technique
• Variational Minimisation goal

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Regression IR HYPERSPECTRAL EMISSIVITY - ICE
and SNOW Sample Max/Min Mean computed from
synthetic radiance sample
Emissivity
Wavenumber
From Lihang Zhou
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Surface Emissivity (e) Estimation Methods

• JCSDA IR Sea Surface Emissivity Model (IRSSE)
• Initial NCEP IRSSE Model based on Masuda et al.
  (1998)

Updated to calculate Sea Surface Emissivities via
Wu and Smith (1997) Van Delst and Wu (2000)
Includes high spectral resolution (for
instruments such as AIRS) Includes sea surface
reflection for larger angles
JCSDA Infrared Sea Surface Emissivity Model
Paul Van DelstProceedings of the 13th
International TOVS Study ConferenceSte. Adele,
Canada, 29 October - 4 November 2003
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Minimum Variance IR HYPERSPECTRAL EMISSIVITY -
Water
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Minimum Variance IR HYPERSPECTRAL EMISSIVITY -
Water
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AIRS SST Determination
Use AIRS bias corrected radiances from GSI AIRS
channels used are 119 129 (11)154 167
(14)263 281 (19) Method is the minimum
(emissivity) variance technique
Channels used in Pairs 119, 120 120, 121 121,
122 . . etc
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For a downward looking infrared sensor
where I?, e?, B?, TS, t?(z1, z2), Z and T(z) are
observed spectral radiance, spectral emissivity,
spectral Planck function, the surface
temperature, spectral transmittance at wavenumber
? from altitude z1 to z2, sensor altitude z, and
air temperature at altitutide z respectively.
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The solution can be written as
Where ROBS is the observed upwelling radiance, N?
represents the upwelling emission from the
atmosphere only and N? represents the downwelling
flux at the surface. The symbol denotes the
effective quantities as defined in Knuteson et
al. (2003).
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The SST is the TS that minimises
i1,43
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Summary The introduction of AIRS hyperspectral
data into environmental prognosis centers has
provided improvements in forecast skill. Here
we have noted initial results where AIRS
hyperspectral data, used within stringent
operational constraints, have shown significant
positive impact in forecast skill over both the
Northern and Southern Hemisphere for January
2004. We have also noted the improvement gained
from using AIRS at a spatial density greater than
that used generally for operational NWP.
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Summary The modeling of surface emissivity in
the CRTM and in a number of related studies have
also commenced to improve our use of AIRS data
over land, water and ice. Initial estimates of
emissivity and skin SST based on hyperspectral
satellite observations in the IR indicate
significant potential for further improving our
current estimate of operational skin temperature.

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Conclusion Given the opportunities for
enhancement of the assimilation system and the
resolution of the hyperspectral data base, the
results here indicate an opportunity to further
improve current analysis and forecast systems
through the application of hyperspectral data.
i.e. further improvements are expected through
use of higher spectral and spatial resolution
data. Further improvements may also be
anticipated through use of data over land,
cloudy data and the use of complementary data
such as Moderate Resolution Imaging
Spectroradiometer (MODIS) radiances to better
characterize the AIRS fovs. (Note- all channel
and AIRS/MODIS BUFR )
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The business of looking down is looking up
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