Transcript JCSDA Briefing - Bureau of Meteorology

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
Overview
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JCSDA – Background/Challenge/SST activity
•
Hyperspectral Data Assimilation
•
Hyperspectral emissivity/SST
•
Plans/Future Prospects
•
Summary
JCSDA Partners
Pending
JCSDA Mission and Vision
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•
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
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
Jason
Terra
WindSAT
ICESat
SORCE
Aura
5-Order Magnitude Increase in
satellite Data Over 10 Years
Satellite Instruments
by Platform
NPOESS
METEOP
NOAA
Windsat
GOES
DMSP
Count
Count (Millions)
Daily Upper Air
Observation Count
Year
1990
Year
2010
JCSDA Instrument Database – June 2006
KEY
Current Operations ( *= Assimilated in NWP
Current Testing/Monitoring (Priority 1)
Current Instrument Failure
Not used / Monitoring (Other)
Operations Near Future
Future (Priority 1)
Future (Priority 2-3)
JCSDA Instrument database
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Earth
Radiation
Budget
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3
v
Aerosols
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3
v
Ozone
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1
3
3
Wind
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Precipitation
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Cloud
Temperature
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Humidity
Microwave
IR
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Ocean Surface
GOES
Status
Current
JCSDA Partner Priorities
Primary Information Content
Land Surface
POES
Instrument
F-13
SSM/I *
SSM/T
SSM/T-2
F-14
SSM/I *
SSM/T
SSM/T-2
F-15
SSM/I *
SSM/T
SSM/T-2
F-16
SSM/T
SSM/T-2
SSMI/S
OLS
NOAA-14
MSU*
HIRS/2 *
AVHRR *
SBUV/2 *
SEM
DCS
SARSAT
NOAA-15
AMSU-A *
AMSU-B *
HIRS/3 *
AVHRR/3 *
SEM/2
DCS
SARSAT
NOAA-16
AMSU-A *
AMSU-B *
HIRS/3 *
AVHRR/3 *
SBUV/2 *
SEM/2
DCS
SARSAT
NOAA-17
AMSU-A *
AMSU-B *
HIRS/3 *
AVHRR /3*
SBUV/2 *
SEM/2
DCS
SARSAT
NOAA-18
AMSU-A *
AVHRR *
SBUV *
HIRS/4 *
MHS
Imager *
Sounder *
Visible
Platform
DMSP
UV
Wavelength
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Current
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Current
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Current
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Current
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Current
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Current
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Current
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Current
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3
METEOSAT
GFO
MTSAT
Terra
TRMM
QuikSCAT
TOPEX
JASON-1
AQUA
Envisat
Windsat
Aura
INSAT-3D
Imager
Altimeter*
Imager *
MODIS*
TMI*
VIRS
PR
CERES
Scatterometer *
Altimeter *
Altimeter
AMSR-E
AMSU*
HSB
AIRS*
MODIS*
Altimeter*
MWR
MIPAS
AATSR
MERIS
SCIAMACHY
GOMOS
Polarimetric
radiometer
OMI
MLS
Imager
Current
Current
Current
Current
Current

Current
Current
Current
Current
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v
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MVISR
Current
VISSR
GPS
GPS
IASI
ASCAT
GRAS
HIRS
AMSU
MHS
GOME-2
AVHRR
VIIRS
CRIS
OMPS
ATMS
GIFTS
MIRAS
VIIRS
CRIS
ATMS
CMIS
GPSOS
APS
ERBS
Altimeter
OMPS
SEM
TSIS
Doppler lidar
GMI
DPR
ABI
HES
DWL
Current
Current
Current
2006
ADM
GPM
GOES R
SST
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TPW
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2007
FY-1
EO-3/IGL
SMOS
NPOESS
v
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Current
FY- 2
CHAMP
COSMIC
METOP
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v
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v
v
v
Sounder
NPP
TPW
TPW
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Current
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Current
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2009

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2009
2007
2013
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SST
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TPW
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SST
SST
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Polar
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Polar
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v
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2012

2013
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*
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2009
2010
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3
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v
v
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v
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Satellite Data used in NWP
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•
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•
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•
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•
•
•
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
…
>32 instruments
Sounding data used operationally within the
GMAO/NCEP Global Forecast System
AIRS
On
HIRS sounder radiances
14 15 16 17 -
AMSU-A sounder radiances
15 - on
16 - on
17 - off
18 - on
AQUA
14 - on
15 - on
16 - on
17 - on
MSU
AMSU-B sounder radiances
on
off
off
on
GOES sounder radiances
10 - on
12 - on
SBUV/2 ozone profile and total ozone
16 - on
17 - on
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
Note: ADM – OSSEs Completed
~ 9 new instruments
Major Accomplishments
•
•
•
•
•
•
•
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•
•
•
•
•
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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
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: …. . . . . . .
USE OF AIRS HYPERSPECTRAL
RADIANCES
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 …..
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
Progress
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•
•
•
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.
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
IMPROVED COMMUNITY RADIATIVE TRANSFER MODEL
CRTM
OPTRAN-V7 vs. OSS for AIRS channels
0.4
OSS
RMS difference (K)
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1
201
401
601
801
1001 1201 1401 1601 1801 2001 2201
AIRS channel num ber
0.4
0.35
OPTRAN
rms(K)
0.3
0.25
0.2
0.15
0.1
0.05
0
1
201
401
601
801
1001 1201 1401 1601 1801 2001 2201
AIRS channel number
AQUA
Hyperspectral Data
Assimilation
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
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
Improved NCEP SST Analysis
Xu Li, John Derber
EMC/NCEP
 Progress
 SST physical retrieval code has been merged into
GSI and provided to NCEP marine branch for
operational use
SST Analysis with GSI: Diurnal Variation signal and comparison with RTG Analysis
04/07/2005 (7th day of GSI SST analysis)
NCEP Operational RTG Daily Analysis
GSI Analysis (Daily Mean of 4 6-h analysis.)
 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
GSI: (00Z – Daily Mean)
GSI: (12Z – Daily Mean)
 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
24
Physical/Variational SST Retrieval Formulation
Cost Function:
1
1
1
1
1
2
2
f
o 2
J  J b  J o  [ 2 (Ts ) 2 
(

T
)

(

Q
)
]

[(
T


T
)

T

a
a
b ,i
b ,i
b ,i ]
2
2
2
2 s
2 a
2 q
2 i  b ,i
Ts  Tsa  Ts f , Ta  Taa  Taf , Qa  Qaa  Qaf
Tb ,i  T  T 
a
b ,i
f
b ,i
Tb ,i
Ts
Ts 
Tb ,i
Ta
Ta 
Tb ,i
Qa
Qa
Tb,i , Ts , Ta ( z),Qa ( z) is brightness temperature (radiance), skin temperature,
atmospheric temperature vertical profile and atmospheric water vapor vertical
profile respectively. Tb,fi is calculated with radiative transfer model.
Tb ,i
,
Tb ,i
,
Tb ,i
is the sensitivity of Tb ,i to Ts , Ta ( z),Qa ( z) respectively.
Initially, the Ta , Qa 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 a ,f ,o represents analysis, first guess and
observation respectively. Lower-subscription i means the channel index.
Ts
Ta
Qa
 b2,i , s2 , a2 , q2 is the error variance of Tb,i , Ts , Ta and Qa respectively
The solutions of Ts , Ta , Qaare solved by minimizing cost function J
Improved NCEP SST Analysis
Xu Li, John Derber
EMC/NCEP
SST Analysis with GSI: Diurnal Variation signal and comparison with RTG Analysis
04/07/2005 (7th day of GSI SST analysis)
NCEP Operational RTG Daily Analysis
GSI Analysis (Daily Mean of 4 6-h analysis.)
GSI: (00Z – Daily Mean)
AMS 2006 - Future National Operational Environmental Satellites Symposium
GSI: (12Z – Daily Mean)
Risk Reduction for NPOESS Using Heritage Sensors
26
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
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
The Trial
•
•
AIRS related weights/noise optimised
Used NCEP Operational verification scheme.
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 > 2000 cm-1 Downweighted
 Wavenumber > 2400cm-1 Removed
AIRS data coverage at 06 UTC on 31 January 2004. (Obs-Calc. Brightness
Temperatures at 661.8 cm-1are shown)
Figure 5.Spectral locations for 324 AIRS selected
channel data distributed to NWP centers.
Table 2: AIRS Data Usage per Six Hourly Analysis Cycle
Number of AIRS Channels
Data Category
Total Data Input to Analysis
~200x106 radiances (channels)
Data Selected for Possible Use
~2.1x106 radiances (channels)
Data Used in 3D VAR Analysis(Clear Radiances) ~0.85x106 radiances (channels)
S. Hemisphere 1000 mb AC Z
20S - 80S Waves 1-20
1 Jan - 27 Jan '04
1
Anomaly Correlation
0.95
0.9
0.85
Ops
0.8
Ops+AIRS
0.75
0.7
0.65
0.6
0
1
2
3
4
5
6
7
Forecast [days]
Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and
without (Ops.) AIRS data, Southern hemisphere, January 2004
S. Hemisphere 500mb AC Z
20S - 80S Waves 1-20
1 Jan - 27 Jan '04
1
Anomaly Correlation
0.95
0.9
0.85
Ops
0.8
Ops+AIRS
0.75
0.7
0.65
0.6
0
1
2
3
4
5
6
7
Forecast [days]
Figure 1(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without
(Ops.) AIRS data, Southern hemisphere, January 2004
Anomaly Correlation
500 mb Anomaly Correlation
Southern Hemisphere
5 Day Fcst
0.95
0.85
0.75
Ops
0.65
Ops+AIRS
0.55
0.45
2
4
6
8
10
12
14
16
18
20
22
Day
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
N. Hemisphere 1000 mb AC Z
20N - 80N Waves 1-20
1 Jan - 27 Jan '04
1
Anomaly Correlation
0.95
0.9
0.85
Ops
0.8
Ops+AIRS
0.75
0.7
0.65
0.6
0
1
2
3
4
5
6
7
Forecast [days]
Figure3(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and
without (Ops.) AIRS data, Northern hemisphere, January 2004
N. Hemisphere 500 mb AC Z
20N - 80N Waves 1-20
1 Jan - 27 Jan '04
1
Anomaly Correlation
0.95
0.9
0.85
Ops
0.8
Ops+AIRS
0.75
0.7
0.65
0.6
0
1
2
3
4
5
6
7
Forecast [days]
Figure 3(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without
(Ops.) AIRS data, Northern hemisphere, January 2004
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
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.
AIRS Data Assimilation
Impact of Data density...
10 August – 20 September 2004
N. Hemisphere 500 mb AC Z
20N - 80N Waves 1-20
10 Aug - 20 Sep '04
Anomaly Correlation
1
0.95
1/18fovs AIRS
0.9
allfovs AIRS
0.85
0.8
0.75
0.7
0.65
0.6
0
1
2
3
4
Forecast [days]
5
6
7
AIRS Data Assimilation
Impact of Spectral density...
10 August – 20 September 2004
Day 5 Average Anomaly Correlation
Waves 1- 20
2 Jan - 15 Feb 2004
0.86
0.855
control
0.85
short airs
0.845
airs-152ch
0.84
airs-251ch
0.835
0.83
nh 500
sh 500+.04 nh 1000+.04 sh 1000+.1
AIRS Data Assimilation
AIRS in the GSI...
1 January – 15 February 2004
AIRS – GSI, v2, …
GSI Contral 1/18 fovs v all fov AIRS
S. Hemisphere 500 hPa AC Z
20S - 80S Waves 1-20
1 Jan - 15 Feb '04
Anomaly Correlation '
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0
1
2
3
4
Forecast [day]
GSI_Control
GSI + AIRS
5
6
7
AIRS Data Assimilation
Application of AIRS Radiances
over land ,water and ice
Surface Emissivity (ε) Estimation
Methods
•
IRSSE Model
•
Geographic Look Up Tables (LUTs) - CRTM
•
Regression based on theoretical estimates
•
Minimum Variance, provides Tsurf and ε *
•
Eigenvector technique
•
Variational Minimisation – goal
Emissivity
Regression IR HYPERSPECTRAL EMISSIVITY - ICE and SNOW
Sample Max/Min Mean computed from synthetic radiance sample
Wavenumber
From Lihang Zhou
Surface Emissivity (ε) 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 Delst
Proceedings of the 13th International TOVS Study Conference
Ste. Adele, Canada, 29 October - 4 November 2003
Minimum Variance IR HYPERSPECTRAL EMISSIVITY - Water
Wavenumber [cm -1]
Calculated
CRTM
1200
1150
1100
1050
1000
950
900
850
800
1
0.98
0.96
0.94
0.92
0.9
0.88
0.86
750
Emissivity
Averaged Emisivity Calculations over Ocean
Minimum Variance IR HYPERSPECTRAL EMISSIVITY - Water
AIRS Averaged Surface Emissivity
12.18 Micron
0.99
0.985
Emissivity
0.98
0.975
0.97
0.965
0.96
0.955
0.95
0
10
20
30
40
Scan Angle
AIRS Calculated
CRTM Calculated
50
60
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
For a downward looking infrared sensor:
 z, Z 


dz    B TS    0, Z  
I    B T z 
Z
0
z
 z, Z 
1     0, Z  B T z 
dz
z
0
where Iν, εν, Bν, TS, τν(z1, z2), Z and T(z) are observed spectral radiance, spectral
emissivity, spectral Planck function, the surface
temperature, spectral transmittance
I
at wavenumber ν from altitude z1 to z2, sensor altitude z, and air temperature at
altitutide z respectively.
The solution can be written as :
ˆ 
R


 N   N 

ˆ
 B TS   N 
OBS


 
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).
The SST is the TS that minimises :
2





 i i1
i=1,43
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.
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.
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 )
The business of looking down
is looking up