Transcript Implementing GNR analysis on the NI PXI

Navy’s MURI Impact on UW
Hyperspectral Activities
Allen Huang
Cooperative Institute for Meteorological Satellite Studies (CIMSS)
Space Science & Engineering Center (SSEC)
Univ. of Wisconsin-Madison
5th Workshop on Hyperspectral Science of UW-Madison MURI, Airborne, LEO, and
GEO Activities
The Pyle Center
University of WisconsinMadison
702 Langdon Street, Madison (608-262-1122)
7-9 June 2005
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UW’s road to the Hyperspectral (Next
Generation) Sounders
HES (~1600; GEO; O)
GIFTS (~1600; GEO; E)
CrIS (~2215; LEO; O)
UW has played a
IASI (~8000; LEO; O)
significant roles in the
AIRS (~2200; LEO; E)
past, current, and future
Hyperspectral Sounders
NAST-I (8220; Airborne)
(labeled in green)
IMG (18400; LEO; E)
S-HIS (4840; Airborne)
GOES Sounder (18; GEO; O)
(# of spectral bands)
HIS (4492; Airborne)
O: Operational
VAS (12; GEO; O)
E: Experimental
VTPR, HIRS (18; LEO; O)
IRIS (862; LEO; E)
1978
2012
Time
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UW’S Hyperspectral End-to-End
Simulation Effort
Mesoscale
Modeling
Validation
Profiles
Clouds
Surface temp
Wind
Profile Tracking
Radiative Transfer
Modeling
Top of Atmosphere
radiances
FTS Simulator
Interferograms
Trade Study
Compression
Instrument Design
Compression Impacts
Compressed
Data (Rad. &Counts)
Wind
Retrieval
Calibration
Off-Axis
Normalization
Spectra
Normalized INFGs
Profiles
: Outputs
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Navy’s MURI Impact on UW
Hyperspectral Activities
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Navy’s MURI Impact on UW
Hyperspectral Activities
Current UW Direct Broadcast
End-to-End Processing Capability
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Single-scattering Properties of Ice Crystals-Database and parameterization
Yang, P., H. Wei, H.-L. Huang, B. A. Baum, Y. X., Hu,
G. W. Kattawar, M. I. Mishchenko, and Q. Fu, 2004:
Scattering and absorption property database for
nonspherical ice particles in the near- through farinfrared spectral region, Appl.Opt. (accepted).
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Bulk Scattering Models
Available for Multiple Instruments
Provide bulk properties (mean and std. dev.) evenly spaced in Deff from 10
to 180 m for
asymmetry factor
phase function
single-scattering albedo
extinction efficiency & cross sections
IWC
Dm
Models available at http://www.ssec.wisc.edu/~baum for
IR Spectral Models (100 to 3250 cm-1)
MODIS
AVHRR
VIRS
MAS (MODIS Airborne Simulator)
ABI (Advanced Baseline Imager)
AATSR
MISR
POLDER (Polarization)
SEVIRI (Spinning Enhanced Visible InfraRed Imager)
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UW Hyperspectral Sounder Simulator & Processor (HSSP)
Simulator - Radiance and Model Component
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UW Hyperspectral Sounder Simulator & Processor (HSSP)
Simulator - Radiance and Model Component
Effect/Feature
Included
•Cloud Microphysics
yes
•Single Scattering Parameterization
Partial
•DISORT
yes
•Cloud Layer Albedo & Transmittance Par. Partial
•Fast Cloudy RT Model
Partial
•Atmospheric profile data base
yes
•LBLRTM
yes
•Water Vapor Spectroscopy
yes
•Fast Clear RT Model
yes
•Adjoint operator
yes
•Tangent Linear
yes
•Ocean Surface Emissivity Model
yes
•Land Surface Emissivity Model
not yet
•Aerosol Parameterization
not yet
•Solar Spectrum
not yet
•RT Model validation
partial
•RT Model consolidation
no
Mesoscale NWP MODEL
yes
•Improved Cloud Physics in NWP
no
Notes
Measurements, NWP model output
ongoing effort
ongoing effort
ongoing effort
under development
ongoing effort
PLOD
MATLAB version
MATLAB version
IRSSE Model (Van Delst)
under development
under development
ongoing effort
coordination: PLOD; RTTOV; OPTRAN; OSS
MM5 and WRF
cloud spectral bin modeling
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Radiative transfer modeling of
atmospheric gases absorption
“ LBLRTM based PLOD fast model”
LBLRTM runs:
• HITRAN ‘96 + JPL extended
spectral line parameters
• CKD v2.4 H2O continuum
Spectral Characteristics:
• ~586-2347 cm-1
• ~0.8724 cm MOPD
• Kaisser Bessel #6 apodization
Fast Model:
Dust/Aerosol
• 32 profiles from
NOAA database
• 6 view angles
• AIRS 100 layers
• Fixed, H2O, and O3
• AIRS PLOD predictors
Run time:
• ~0.8 Sec on a 1 GHz CPU
Temp.
Surface
Type
Ozone
CO Temp.
Water Vapor
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Radiative transfer approximation of
single cloud layer model
gaseous
trans. / OD
I I0 c + Icc + I + IRc
Layer
#
1
Pressure
(hPa)
I
I c
2
Icc
3
I 
 
0
1
Pc
c c
Ps
s s
IRc
0
4
5
6
7
I
I
I
98
99
100
101
1100
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Two layer cloud model from Texas A&M
coupled with UW/CIMSS clear-sky model
3 ice cloud models, 1 water cloud model
100-3246 1/cm (~3-100 um)
Tropical
De = 16-126 um
Mid-latitude
De = 8-145 um
Polar
De = 1.6-162 um
Water-spheres
De = 2-1100 um
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A fast infrared radiative transfer model
(FIRTM2) for overlapping cloudy
atmospheres
Niu, J., P, Yang, H.-L. Huang, J. E. Davies, J. Li, B. A. Baum, and Y. Hu,
2005: A fast infrared radiative transfer model for
overlapping cloudy atmospheres. J. Quant.
Spectroscopy & Radiative Transfer (to be submitted).
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How to extract the cloud information?
• AIRS sub-pixel cloud detection and
characterization using MODIS data (Li et al.
2004a)
• Cloud property retrieval from AIRS radiances
(Li et al. 2004b; 2005) with the help of MODIS
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An Aerosol Database
Database, 18 classes and 28 components [adapted from Levoni et al.,1997]
describes aerosol physical-chemical properties using:
Size Distribution:
Lognormal distribution
dN(r)
N
(log r r0 )2

exp[
]
2
dr r ln10 2 log 
2(log  )
dN(r)
Modified gamma distribution
 Nr  exp(br  )
dr
Chemical composition:Complex refractive index
 Dependence of wavelengths
 particle, change with relative humidity
 Hygroscopic
 Internal mixture
Shape: Spherical ( Mie theory). We plan to extend the study
by considering nonspherical particles
Concentration: Any
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UW Hyperspectral Sounder Simulator & Processor (HSSP)
Simulator - Sensor Component
Effect/Feature
Included
•Instrument Emission
•Instrument Responsivity
•Numerical Filter
•Instrument Phase
•Phase variation across FPA
•Off-axis OPD sampling
•ILS variations
•pixel-to-pixel offset variations
•pixel-to-pixel gain variations
•pixel operability
•FPA center not aligned with FTS axis
•LW/SMW FPA misalignment
•Detector non-linearity
•Detector noise
•Photon noise
•Quantization noise
•OPD scan mirror velocity variation
•OPD scan mirror tilt
•Diffration blur
•Jitter blur
yes
yes
yes
yes
not yet
yes
yes
yes*
yes*
not yet
yes
no
no
yes
yes*
yes*
no
no
no
no
Notes
filter function set to unity
varies linearly with n
12%(LW), 5%(SMW) random variation
8-40%(LW), 2-5%(SMW) of full well depth
1-2 pixels, non integer
retrieval issue
small
small
small
*Currently being implemented
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UW Hyperspectral Sounder Simulator & Processor (HSSP)
Processor - Measurement & Retrieval/Product Component
Effect/Feature
Included
•Calibrated radiances
yes
•Geo-location
yes
•Total sensor noise
yes
•Diffraction blur
partial
•4-km sampling
yes
•15 to 30 minutes sampling
yes
•Clear radiances
yes
•Cloudy radiances
yes
•Aerosol/Dust radiances
not yet
•Ocean emissivity
yes
•Land emissivity
not yet
•Clear regression retrieval
yes
•Clear physical retrieval
yes
•Cloudy retrieval down to cloud level
partial
•Cloudy retrieval – transparent clouds
not yet
•Altitude resolved water vapor wind
yes
•3D water vapor wind
not yet
•Cloud detection
partial
•Cloud clearing without microwave
partial
•
Cloud property
not yet
•
Lossless & Lossy data compression partial
•
Measurement Noise Estimation yes
Notes
generate sensor spectral measurements
based on nominal geo orbit
mainly random detector noise
simulated to demonstrated band to band reg. Error effect
MM5 meso-scale run
MM5 meso-scale run
Latest PLOD fast clear model run
Water & Ice Clouds (includes size effect)
Extinction modeling underdevelopment
IRSSE model
underdevelopment (UH-UW)
demonstrated by simulation, air/space borne
developed under testing
demonstrated by simulation and airborne
under design
demonstrated by simulation and airborne
under development
under development
under development
under design
under development
ongoing effort
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AIRS Std.
Operational Product
CIMSS
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AIRS/MODIS Synergistic C.C. can Supplement
AIRS/AMSU C.C. Especially over Desert Region
AIRS/AMSU C.C.
(3 by 3 AIRS FOV)
V4.0 - Blue
AIRS/MODIS C.C.
(1 by 2 AIRS FOV)
Multi-Ch. - Black
Single-Ch.:
Band 31 – Green
Band 22 - Red
South Africa Granule
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AIRS Absolute Emissivity
Atm. Corr.
Relative
IR Emiss
Ozone
Not Fit
Absolute
IR Emiss
• Squares are using 281 Select AIRS channels only. It Works !!!
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12 m Emissivity
MODIS
July 2003
AIRS
AIRS - MODIS
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Altitude Resolved Water Vapor
Wind Demonstration
GIFTS - IHOP simulation 1830z 12 June 02
GOES-8 winds 1655z 12 June 02
Simulated GIFTS winds (left) versus GOES current oper winds (right)
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Selecting Computing Hardware
• Cluster options were evaluated and found to
require significant time investment.
• Purchased SGI Altix fall of 2004 after extensive test
runs with WRF and MM5.
– 24 - Itanium2 processors running Linux
– 192GB of RAM
– 5TB of FC/SATA disk
• Recently upgraded to 32 CPUs, 10TB storage.
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Model Configuration
• 42 hr simulation initialized at 1200 UTC
23 June 2003
• 290 x 290 grid point domain with 4 km
horizontal spacing and 50 vertical levels
MM5
WRF
• Goddard microphysics
• WSM6 microphysics
• MRF PBL
• YSU PBL
• RRTM/Dudhia radiation
• RRTM/Dudhia radiation
• Explicit cumulus convection
• Explicit cumulus convection
• OSU land surface model
• NOAH land surface model
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Global training database for hyperspectral and multi-spectral
atmospheric retrievals
Suzanne Wetzel Seemann, Eva Borbas
Allen Huang, Jun Li, Paul Menzel
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Non-dimensional Tb Sensitivity to
Atmospheric Temperature
(Thermal Source only)
Clear
sky
Cloudy
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Data Compression Demonstration
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Ground Segment Processing
Demonstration
GIPS Design Elements
• Monitoring, Control, and Data Channels
• Parallel Processing Pipeline Architecture
• Modular Software Component Design
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Navy’s MURI Impact on UW
Hyperspectral Activities
Itemized Impacts
Physical Modeling
Clear Sky RTE Forward Model Enhancement/Improvement
Cloud/Aerosol Microphysical Property Database Development
Cloudy Sky RTE Forward Model Development
Surface Property
High-spatial Resolution NWP Model Simulation
Sensor Measurements Simulation
Level 0 to Level 1 and Level 1 to Level 2 Processing
Algorithm Development & Demonstration
Hyperspectral/Multispectral Synergy
Hyperspectral/Multispectral Applications
Hyperspectral Science Education & Training
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Navy’s MURI Impact on UW
Hyperspectral Activities
Overall Impact
Every Element of a Truly End-To-End
Infrastructure Under Construction at
SSEC/CIMSS of UW-Madison in Support
of NPP/NPOESS & GOES-R Activities
Through Three-Pillar Partnership
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Monday-Thursday 1-4 August 2005
Atmospheric and Environmental Remote Sensing Data
Processing and Utilization: Numerical Atmospheric
Prediction and Environmental Monitoring
3:30 to 5:30 pm Monday 1 August 2005
Panel on Three-Pillar Partnership in Remote Sensing: the Roles of
Government, Industry, and Academia
Moderator: James F. W. Purdom, Colorado State Univ.
Paneilists*: Philip E. Ardanuy, Raytheon Technical Services Co. LLC; Michael J. Crison, Colleen Hartman, National Oceanic and Atmospheric Administration; Henry E.
Revercomb, Univ. of Wisconsin/Madison; Steven W. Running, Univ. of Montana; Merit Shoucri, Northrop Grumman Space Technology
*Tentative commitments at time of publication, subject to change.
This panel, organized by the track and conference chairs of the Remote and In Situ Sensing program track, offers the opportunity to discuss the roles of government, industry,
and academia in the era of NPOESS and GOES-R, these being our nation’s preeminent environmental satellite programs in the coming decades. The revolution in the last 40
years to date in remote sensing that has taken place in the United States could not have occurred without the closest cooperation between these three pillars.
The unrelenting growth in processing complexity and measurement data volume, arising from maturing environmental satellite systems, triggered NOAA and NASA to jointly
task the National Academy of Sciences to conduct an end-to-end review of current practices, including characterization of process weaknesses, assessment of resources and
needs, and identification of critical factors that limit the optimal management of data including the strategic analysis for maximum environmental satellite data utilization. The
Committee on Environmental Satellite Data Utilization (CESDU) was formed in early 2003 to respond to this charge.
CESDU recommended a partnership strategy between the government, industry, and academia (the CESDU report is available from
http://www.nap.edu/openbook/0309092353/html/1.html). This “three-pillar” partnership strategy was identified as a significant factor in the success of ozone retrievals in a
CESDU case study. The strategy for future system acquisitions will be discussed in light of these recommendations.
Short Presentations on:
Government Perspective
Industry Perspective
Academia Perspective
National Academy of Sciences’ CESDU report
Key Discussion Issues:
Contention: Only a fully integrated team--a joint three-pillar partnership--working together in a seamless manner with a relentless determination to excel, will achieve total
user satisfaction and comprehensive data utilization.* Examples from the past * NPOESS partnerships
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