University of Wisconsin - Madison (UW)University of Wisconsin - Madison (UW)University of Hawaii (UH) University of Hawaii (UH)

Texas A& M (TAMU)Texas A& M (TAMU)University of Colorado at Boulder (CU)University of Colorado at Boulder (CU)

University of Alabama in Huntsville (UAH)University of Alabama in Huntsville (UAH)MURIMURI

“Physical Modeling for Processing

Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) – Indian Ocean METOC

Imager (IOMI) Hyperspectral Data”

RevisedRevised Tasks Tasks1 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

Co-I and Subcontract TasksCo-I and Subcontract Tasks

•Prof. Paul Lucey (UH-HIGP)Surface Characterization

•Prof. Ping Yang (TA&M)Cloud Modeling

•Prof. Irina Sokolik (CU)Aerosol/Dust Modeling

•Prof. Gary Jedlovec & Sundar Christopher (UAH)Cloud and Aerosol products, & Wind Tracking Analysis

Surface CharacterizationSurface 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.

Surface CharacterizationSurface 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

Surface CharacterizationSurface 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

Cloud ModelingCloud Modeling

Subcontractor: Prof. Ping Yang (University of Texas A&M)

Tasks and Goals:

• Develop State of the Art Cloud Model for GIFTS/IOMI1. Water Cloud Radiative Property Modeling2. Ice Cloud Radiative Property Modeling3. Full Fast Physical Cloudy Radiative Transfer Modeling4. Cloud Property Retrieval

Cloud ModelingCloud Modeling

Subcontractor: Prof. Ping Yang (University of Texas A&M)

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

Cloud ModelingCloud ModelingSubcontractor: Prof. Ping Yang (University of Texas A&M)

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

Aerosol/Dust ModelingAerosol/Dust ModelingSubcontractor: 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

Cloud and Aerosol Product ResearchCloud 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 characterizationof 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

Cloud and Aerosol Product ResearchCloud 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 exploitsthe high spectral resolution information content of theGIFTS/IOMI to improve the detection and characterizationof 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

SummarySummary1. 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