University of Wisconsin Madison UW University of Hawaii UH Texas A

1
University of Wisconsin - Madison (UW)University
of Hawaii (UH) Texas A M (TAMU)University of
Colorado at Boulder (CU)University of Alabama in
Huntsville (UAH)MURI

• Physical Modeling for Processing
• Geosynchronous Imaging Fourier Transform
  Spectrometer (GIFTS) Indian Ocean METOC Imager
  (IOMI) Hyperspectral Data

2
Revised Tasks

• 1 Mathematical Quantification of Useful
  Hyperspectral Information
• 2 Radiative Transfer Modeling
• Clear Sky Emission/Absorption
• Atmospheric Particulate Emission/Absorption
• Surface Emission/Absorption
• Cloud modeling
• Aerosol/Dust Modeling
• 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/Dusts
• Derived (Second Order) Products
• visibility and Clouds

3
Co-I and Subcontract Tasks

• Prof. Paul Lucey (UH-HIGP)
• Surface Characterization
• Prof. Ping Yang (TAM)
• Cloud Modeling
• Prof. Irina Sokolik (CU)
• Aerosol/Dust Modeling
• Prof. Gary Jedlovec Sundar Christopher (UAH)
• Cloud and Aerosol products, Wind Tracking
  Analysis

4
Surface Characterization

• Co-Investigator Prof. Paul Lucey (UH-HIGP)
• Tasks and Goals
• Surface Materials Properties
• Airborne hyperspectral data reduced to emissivity
  
• Laboratory data collection of materials spectral
  properties
• Directed airborne hyperspectral data collection
• Specialized surface materials properties of
  interest to GIFTS/IOMI MURI
• Data collections in support of atmospheric model
  validation
• Hyperspectral analysis methodologies
• Target detection and surface materials
  classification algorithms.

5
Surface Characterization

• Co-Investigator Prof. Paul Lucey (UH-HIGP)
• Progress
• Continuing compilation of non-classified airborne
  hyperspectral data sets in units of surface
  emissivity in accessible on-line form
• Continued collection of laboratory data in
  support of surface emissivity library
• Test data collection of airborne hyperspectral
  data in Hawaii for atmospheric model validation.
• Plan-Completion of on-line data base with simple
  data ingestion (may add Hawaii laboratory spectra
  to Arizona State University data base as an
  alternative).
• Continue collection of directed validation
  airborne data runs

6
Surface Characterization

• Co-Investigator Prof. Paul Lucey (UH-HIGP)
• Planned Tasks
• To complete the surface materials data archive
  and provide access to GIFTS-MURI team members and
  other users.
• To finalize our plan to reproduce the Arizona
  State library model, or to contribute our
  laboratory data to this archive, and implement
  the final disposition of spectra
• On complete reduction of the field data, we will
  plan a model validation run with MURI team
  members, this time focussing on aerosols and
  water vapor

7
Cloud Modeling

• Subcontractor Prof. Ping Yang (University of
  Texas AM)
• 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
• 4. Cloud Property Retrieval

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Cloud Modeling

• Subcontractor Prof. Ping Yang (University of
  Texas AM)
• Progress (three deliveries)
• Optical properties of Water Clouds in spectral
  regions of
• 685-1130 and 1650-2250 cm-1
• Optical Properties of Ice clouds in longwave
  infrared
• Window (8-13 µm) region
• Optical Properties of Ice clouds in 1667-2500
  cm-1 region

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Cloud Modeling

• Subcontractor Prof. Ping Yang (University of
  Texas AM)
• Planned Tasks
• 1. Improve cloud optical models (in particular,
  for cirrus clouds)
• Current delivery simplifies the geometry of ice
  crystals
• More realistic ice crystal habits will be used
• Improve the efficiency of computational model and
  increase
• spectral resolution in light scattering
  computation
• 2. Mixed-phase cloud
• How to model the situation when ice crystals and
  supecooled
• liquid droplets coexist
• 3. Sensitivity of infrared radiance to cloud
  optical properties
• 4. Explore algorithms to retrieve cloud
  properties

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Aerosol/Dust Modeling

• Subcontractor Prof. Irina Sokolik (University of
  Colorado)
• Tasks and Goals
• Establish a framework for the development of a
  new physically-based
• treatment of mineral dust for IR hyperspectral
  remote sensing
• analyze NAST-I spectra along with other
  observations performed in
• the East China Sea region during Spring of
  2001 to identify an Asian
• dust spectral radiative signature
• perform detailed forward modeling to determine
  the sensitivity of
• GIFTS observations to regional dust properties
  and develop an
• atmospheric correction algorithm in the dust
  laden conditions
• develop and test a new algorithm to retrieve
  dust from GIFTS observations

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Cloud and Aerosol Product Research

• Subcontractor Prof. Gary Jedlovec Prof. Sundar
  Christopher
• (University of Alabama in Huntsville)
• Tasks and Goals
• The development of a real-time cloud product that
  exploits
• the high spectral resolution information content
  of the
• GIFTS/IOMI to improve the detection and
  characterization
• of clouds, aerosols, and surface features
• 1. Cloud detection and product generation
• Description of the Bi-spectral Threshold (BTH)
  method for GOES
• Example comparing GOES product to MODIS cloud
  product
• Applications to GIFTS/IOMI

12
Cloud and Aerosol Product Research

• Subcontractor Prof. Gary Jedlovec Prof. Sundar
  Christopher
• (University of Alabama in Huntsville)
• Tasks and Goals
• The development of a real-time cloud product that
  exploits
• the high spectral resolution information content
  of the
• GIFTS/IOMI to improve the detection and
  characterization
• of clouds, aerosols, and surface features
• 2. Feature tracking accuracy
• Sources of satellite wind track errors
• Lower limit on wind tracking accuracy
• definition of Tracking Error Lower Limit (TELL)
  parameter
• Spatial-temporal resolution trade-offs TELL
  diagrams

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Summary

• 1. Surface modeling and Characterization
• Progress been made in compilation of surface data
  base
• and laboratory surface emissivity library
• Test data set for validation of modeling and
  algorithm
• 2. State of the Art Cloud Modeling
• Fast parameterization of both ice and water cloud
  property
• 3. State of the Art Aerosol/Dust Modeling
• Aerosol/dust modeling expert join MURI team
• 4. Geo (high temporal) Cloud and Aerosol Products
  Research
• The use of Geo data experts join MURI team