Physical Modeling for Processing Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Indian Ocean METOC Imager (IOMI) Hyperspectral Data
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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 Enhanced 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 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
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: Surface Emissivity Simulation Estimate of MODIS surface emissivity Cloud Detection and Removal Emissivity assignment Provide MURI team with surface emissivity model Planned Tasks: Dynamic emissivity Vegetation senescence Surface moisture Ground truth using data from ASTER
6 Cloud Modeling Subcontractor: Prof. Ping Yang (University of Texas 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 4. Cloud Property Retrieval
7 Cloud Modeling Subcontractor: Prof. Ping Yang (University of Texas A&M) Progress: Modeling of the single-scattering properties of cirrus and water clouds Modeling of bulk optical properties of cirrus and water clouds Developed a fast radiative transfer model for clouds
8 Cloud Modeling Subcontractor: Prof. Ping Yang (University of Texas A&M) Planned Tasks: Retrieval of Cirrus Properties from High Spectral Resolution Infrared Observations: Sensitivity study Simultaneous retrieval of the optical thickness and effective particle size of cirrus clouds
9 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 high resolution spectral remote sensing Progress: Development of new aerosol models: Developed and tested new high resolution optical models of Asian dust and aerosol species. Forward modeling&sensitivity studies: Linked kcarta and DISORT and modified to handle the new aerosol models. NAST-I data analysis: Collected and analyzed NAST-I & various data (meteorological, satellite, lidar network, etc.).
10 Aerosol/Dust Modeling Subcontractor: Prof. Irina Sokolik (University of Colorado) Planned Tasks: Examination of Asian dust outbreaks based on observational data and MM5 simulations Continue to provide and refine dust/aerosol parameterization model
11 Cloud and Aerosol Product Research Subcontractor: Prof. Gary Jedlovec & Prof. Sundar Christopher (University of Alabama in Huntsville) Tasks and Goals: Identify cloud and surface characteristics in high spectral resolution data which best delineate clouds, aerosols, and surface properties from one another. Develop a cloud detection algorithm that exploits high spectral resolution measurements such as GIFTS/IOMI
12 Cloud and Aerosol Product Research Subcontractor: Prof. Gary Jedlovec & Prof. Sundar Christopher (University of Alabama in Huntsville) Progress: UAH student (Kevin Laws) complete M.S. refining cloud detection algorithm with GOES demonstrates value of spatial and temporal thresholds in difference images New UAH graduate student (Nicole Slodysko) began thesis work with analysis of AIRS data for cloud spectral properties Planned Tasks: Cloud spectral features (slope and offsets) will be used to develop a robust cloud detection algorithm for high spectral resolution data
13 Summary 1. Surface modeling and Characterization Provided a surface emissivty simulation model 2. State of the Art Cloud Modeling Provided a fast cloudy radiative transfer computational package 3. State of the Art Aerosol/Dust Modeling Provided a aerosol/dust model 4. Geo (high temporal) Cloud and Aerosol Products Research The use of Geo high spectral resolution data for cloud detection
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