Planned Hyperspectral Imaging &
Sounding Researches for Indian Ocean
Under GIFTS-IOMI Project
Allen Huang - PI
John M., Jun L., Steve A., Wayne F., Fred W., Bormin H.,
Dave T., and Chris V.
CIMSS, Univ. of WI-Madison
Paul L. HIGP, Univ. of Hawaii
Ping Y., Univ. of Texas A&M
Gary J. & Sundar C., Univ. of Alabama-Huntsville
Irina S., Univ. of Colorado at Boulder
Advisors: Walt McKeown & Bill Smith
GIFTS-IOMI Measurement Concept
Combine Two Measurement Technologies On a
Geosynchronous Satellite to Obtain Four
Dimensional Observations of the Atmosphere
• (Horizontal) Large area format focal
plane detector arrays provide near
instantaneous wide geographical
coverage
• (Vertical) Michelson interferometer
provides high spectral resolution
• (Temporal) Geosynchronous orbit
provides high time resolution (i.e.,
motion observations)
GIFTS Performance Relative to Current
and Future GOES Sounders Options
Parameter
GOES (I-M)
ABS
GIFTS
Channels
Detector Elements
Spatial
Footprint/Spacing
Coverage Rate1
Performance2
19
4
8 / 10
1452
256
10 / 10
1724
16384
4/4
~ 42 min
1
~ 20 min
4
~ 7 min
66
1
Per 3000-km x 3000-km area
2 Information content per area resolution per coverage rate
relative to GOES (I-M)
Original Proposed Tasks for 2001-2006
1 Mathematical Quantification of Useful Hyperspectral
Information
2 Radiative Transfer Modeling
• Clear Sky Emission/Absorption
• Atmospheric Particulate Emission/Absorption
• Surface Emission/Absorption
3 Mathematical Retrieval Algorithm Development
• Atmospheric Parameters
• Suspended Particulate Detection and Quantification
• Sea Surface Temperature
• Surface Material Identification
4 Product Research
• Ocean Surface Characterization
• Lower Tropospheric Temperature, Moisture and Winds
• Surface Material Products
• Aerosols
• Derived (Second Order) Products
Revised Tasks for 2001-2006
1 Mathematical Quantification of Useful Hyperspectral
Information
2 Radiative Transfer Modeling
• Clear and cloudy Sky Emission/Absorption
• Atmospheric Particulate (both dust & aerosol) Emission/Absorption
• Surface Emission/Absorption
• Ajoint & Linear Tangent
3 Mathematical Retrieval Algorithm Development
• Atmospheric Parameters
• Suspended Particulate Detection and Quantification
• Sea Surface Temperature
• Surface Material Identification
4 Product Research
• Ocean Surface Characterization
• Lower Tropospheric Temperature, Moisture and Winds
• Surface Material Products
• Aerosols
• Derived (Second Order) Products
•Visibility
Presentation Outline
•Work Breakdown Structure & Personnel Allocation
•Data Simulation
•Clear Fast Forward Modeling
• Algorithm Development / Meteorological Applications
– Temperature/Moisture Profile; Sea Surface Temperatures;
– Winds; Aerosols and Visibility
– Stability and Turbulence; Surface Characterization
• Cloud & Dust/Aerosol Modeling
• Uniqueness of GIFTS/IOMI MURI
• Goal of Tasks: Year 1 and beyond
GIFTS/IOMI WBS & Personnel Allocation
Allen Huang (PI)
John Mecikalski (PM)
1st Order
2nd Order
MURI
Students: Kate LaCasse (DA)
Jon Moskaitis (DA)
seeking others
Jun Li
and others
Dave Tobin
Bormin Huang
John Mecikalski
and others
Retrieval
Algorithms
Forward
Modeling
Information
Content
Numerical
Modeling
Wayne Feltz
and others
Stability &
Turbulence
Paul Lucey
(UH-HIGH)
Steve Ackerman,
I. Sokolik (UC-B)
Dust &
Visibility
Surface
Characterization
Chris Velden
Bormin Huang
PBL Winds
Sea Surface
Temperature
Clouds & Cloud
Modeling
UW-CIMSS Support Staff: Derek Posselt, Hal Wolf,
Leslie Moy, Elizabeth Weise, Erik Olson, Kevin Bagget
Ping Yang
(UT A&M)
G. Jedlovec
(UAH)
GIFTS/IOMI Simulation Flowchart
MesoScale
Model
Atmos. Profiles
Cloud properties
Surface properties
Winds
Raobs,
Retrievals,
etc.
Radiative
Transfer
Model
Top of Atmos.
Radiance
Spectra
FTS
Simulator
Interferograms or Spectra
Radiance or Counts
Signal or Noise
Use of Simulated Data
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
Interferograms
Wind
Retrieval
Calibration
Off-Axis
Normalization
Spectra
Normalized INFGs
Profiles
: Outputs
Temporal, Spectral, and Spatial Information
Content of GIFTS/IOMI Measurements
685 1/cm
900 1/cm
1650 1/cm
1950 1/cm
00:00 Z
00:30 Z
01:00 Z
GIFTS/IOMI Forward Model
Improvements
Co-Investigator: Dr. Dave Tobin (UW-CIMSS)
Tasks and Goals:
• Within the UW-MURI, add functionality to the existing
UW-GIFTS/IOMI fast model, including reflected thermal and
solar radiation terms, variable CO concentration, and the
inclusion of tangent linear and adjoint modules.
• Develop an improved clear sky fast model with state-of-the-art
spectroscopic knowledge and fast model parameterization for
producing simulated GIFTS data and other MURI algorithm
development.
Current GIFTS/IOMI Fast Model characteristics
“ 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
Example Comparison of Observed and Calculated
Scanning-HIS overflight of ARM ground site in north central Oklahoma on 9 Dec 2000
Retrieval Algorithms
Co-Investigator: Dr. Jun Li (UW-CIMSS)
Tasks and Goals:
• Within the UW-MURI, State of the art retrieval algorithm will
be developed which uses heritage and new approaches
•Physical and information based algorithm will be optimized for
efficient processing
•Perform robust testing for optimal temperature and water vapor
sounding products, especially for boundary layer soundings.
850 hPa Global Temperature Retrieval
MODIS
August 23,
2000
AMSU-A
Surface Emissivity Water Vapor Retrieval
MODIS 4 micron band
Vs.
derived surface emissivity
MODIS 8.6 micron band
Vs.
derived 700 mixing ratio
Suspended Matter (Aerosol and Dust)
Co-Investigator: Dr. Steve Ackerman (Director UW-CIMSS)
& Irina Sokolik (Uinv. Of Colorado at Boulder)
Tasks and Goals:
• Improve existing algorithms for use with GIFTS hyperspectral
data for the detection and characterization of atmospheric
S.M.(aerosols).
• Improve existing algorithms to demonstrate ship-based (i.e. carrier
deck) horizontal and slantwise visibility estimates.
AVHRR Dust Detection
AVHRR Band 1
Dust Mask
Northwest African Coast and Adjacent Atlantic
NOAA-11 AVHRR (LAC), April 21, 1992
Hyperspectral Dust Observations
Desert
Red Sea
Boundary Winds
Co-Investigator: Chris Velden (UW-CIMSS)
Tasks and Goals:
• Implement well-tested and proven methods for measuring
winds from geostationary satellite instruments.
• Improve existing algorithms to provide data for low-level wind
shear and turbulence algorithms to describe the oceanic
environment.
GIFTS/IOMI Experimental
Tracking Methods
GIFTS/IOMI Winds
Development Path
• Single channels
• Superchannels
• Altitude-determined fields
from moisture retrievals
• Simulated data
• Real data (Case Studies)
• Real-time demonstration
and validation
Testing and Product Validation
ONR’s Participation
in Future Development
• Case studies
• Statistical analysis using in-situ
observations
• Numerical model impact studies
• NRL forecast office feedback
Using GIFTS/IOMI as the
vehicle, Navy will play a crucial
role in the development of the
next generation geostationary
satellite winds algorithm.
Turbulence & Stability
Co-Investigators: Wayne Feltz (UW-CIMSS) and Dr. John
Mecikalski (UW-CIMSS)
Tasks and Goals:
• Improve existing algorithms for use with GIFTS hyperspectral
data for the detection and characterization of atmospheric
turbulence and stability.
• Demonstrate derived products for ship-based use (combining
estimated turbulence, stability, wind, and aerosols)
AERI Detected Weather Events
Cold Frontal Passages
Elevated Mixed Layers
Moisture Gradient
Return Flow Moisture
RAOB Parcel Energy
Atmospheric Stability Index Validation
Continuous Monitoring of the Boundary Layer
T
Q
P.E.
CAPE
L.I.
Time
Sea Surface Temperatures
Co-Investigator: Dr. Bormin Huang (UW-CIMSS)
Tasks and Goals:
• Improve existing algorithms for use with IOMI hyperspectral
data for estimating surface emissivity and measuring sea-surface
temperatures.
• Develop research algorithms that will lead to efficient Naval
at sea (e.g., engine cooling, atmospheric stability in
boundary layer).
MODIS SST
MODIS
SST, UTC
15:452UTC,
May 2, 2001
(15:45
May 2001)
Cold Labrador Current (<5C)
Warm Gulf Stream (>25C)
GOES SST Estimates
(20-12 UTC 20 May 1998 differences)
Diurnal Variation
NAST-I SST
(Wallops 23 August 1999)
Surface Emissivity Modeling
(Angle and Sea State Dependency)
Surface Emissivity Difference for wind of 16 m/s and
that for wind of 0 m/s (but not the specular surface),
as a function of wavelength and view angle
Numerical Weather Prediction
Co-Investigator: Dr. John Mecikalski (UW-CIMSS)
Tasks and Goals:
• Produce through numerical experiments simulated GIFTS
data “cubes” at GIFTS resolution over large geographical
domains
• Develop the numerical modeling infrastructure necessary to
support the processing of GIFTS data for the development of
meteorological applications. Develop the massively parallelized
computer codes for GIFTS data requirements.
GIFTS/IOMI 10 Second Simulated Measurements
(128 by 128 FOVs)
2250 1/cm
1900 1/cm
650 1/cm
750 1/cm
900 1/cm
1650 1/cm
Surface Characterization
Co-Investigator: Dr. Paul Lucey (UH-HIGP)
Tasks and Goals:
•Modeling of surface emissivity
•Apply UH analysis models to GIFTS simulations and
GIFTS-MURI data sets
•Collect moderate resolution airborne hyperspectral data in
support of MURI software development
•Demonstrate GIFTS/IOMI surface characterization of naval
environment to support battle-space activities
UH Operates Airborne, Field and Laboratory
Hyperspectral Data Collection Systems
All systems are available
for GIFTS MURI
AHI Airborne LWIR
Hyperspectral Imager
Nicolet LWIR
Laboratory FTIR
Designs and Prototypes LWIR
Field FTIR
Hawaii Capabilities Contribute To All Aspects of
Wisconsin MURI Project
MURI
Research
Components
Mathematical
Quantification
of
Hyperspectral
Information
Radiative
Transfer
Modeling
Mathematical
Retrieval
Algorithm
Development
Product
Research
University of Hawaii Contributions
IR Spectral
Phenomenology
Hyperspectral
Data Collection
Provide test
data sets
Hyperspectral
Data
Compression
Methodologies
Leverage
NIMA-funded
UH research to
reduce data
volume
Contribute
experience with
measurement
and modeling of
surfaces
Contribute
ground-truthed
data sets
Integrate modeling/data collection/information
extraction
Cloud Modeling
Subcontractor: Prof. Ping Yang (UT-A&M)
Tasks and Goals:
• Develop State of the Art Cloud Model for GIFTS/IOMI
1. Water Cloud Radiative Property Modeling
2. Ice Cloud Radiative Property Modeling
3. Full Fast Physical Cloudy Radiative Transfer Modeling
Observed Cloud Shapes
Pole
Mid-Latitude
Tropics
Modeled Vs. Observed Cloud Shapes
Observed
(in Black)
Modeled
(in Red)
Theoretical Vs. Experimental Cloud Effects
Old Vs. New
Old
Old
New
New
Measured
Dust Modeling
Subcontractor: Prof. Irina Sokolik (Univ. of Colorado)
Tasks and Goals:
• Develop State of the Art dust/aerosol Model for GIFTS/IOMI
1. Dust Radiative Property Modeling
2. Aerosol Radiative Property Modeling
3. Full Fast Physical Cloudy Radiative Transfer Modeling
Examples of spectral optical properties
of lineral dust mixtures (quartz + clays)
Normalized extinction coefficient
0.018
0.016
0.014
0.012
Mix1
Mix2
Mix3
Mix4
Mix6
Mix7
Mix8
0.01
0.008
more quartz
0.006
0.004
0.002
0
8
8.2
8.4
8.6
wavelengths
8.8
9
Effect of the dust mixture and loading on
brightness temperature
(US 1976 Standard Atmosphere, observation at 100 km, averaging Dn = 0.5 cm-1, dust in the lowest 2.5 km)
(Sokolik, GRL, 2001)
Clear
sky
Dust causes an
“inverse slope”
Year 1 Goals
(through 30 April 2002)
1. Develop improved fast and forward radiative models for
GIFTS data
2. Enhance capabilities to simulate and visualize GIFTS
data “cubes” for simulation experiments
3. Develop numerical modeling and computational infrastructure to support algorithm development
4. Collect GIFTS validation data set for water vapor and
temperature (e.g. International H2O Project, AIRS,
NAST-I)
5. Begin algorithm improvements necessary for use with
GIFTS hyperspectral measurements for atmospheric
and land-surface characterization
5 Year Goals
(through 30 April 2006)
1. Improve our basic understanding of the GIFTS/IOMI
forward modeling and retrieval parameters in order to
optimize the algorithm development and data products in
preparation to meet fleet METOC requirements
2. Demonstrate optimal use of GIFTS/IOMI hyperspectral
measurements and generation of environmental products
3. Enable GIFTS/IOMI data processing to support DOD
strategic Objectives
4. Enhancing DOD tactical environmental-support capability
for naval activities
5. Ready for post-launch prototyping and operational
implementation of GIFTS/IOMI data processing and
product generation