Navy’s MURI Impact on UW Hyperspectral Activities

1. 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

2. 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 significant roles in the past, current, and future Hyperspectral Sounders (labeled in green) IASI (~8000; LEO; O) AIRS (~2200; LEO; E) NAST-I (8220; Airborne) IMG (18400; LEO; E) S-HIS (4840; Airborne) GOES Sounder (18; GEO; O) (# of spectral bands) O: Operational E: Experimental HIS (4492; Airborne) VAS (12; GEO; O) VTPR, HIRS (18; LEO; O) IRIS (862; LEO; E) 1978 Time 2012

3. UW’S Hyperspectral End-to-End Simulation Effort Mesoscale Modeling Radiative Transfer Modeling FTS Simulator Interferograms Profiles Clouds Surface temp Wind Top of Atmosphere radiances Compression Trade Study Profile Tracking Compressed Data (Rad. &Counts) Instrument Design Compression Impacts Wind Validation Off-Axis Normalization Calibration Retrieval Normalized INFGs Spectra Profiles : Outputs

4. Navy’s MURI Impact on UW Hyperspectral Activities

5. Navy’s MURI Impact on UW Hyperspectral Activities Current UW Direct Broadcast End-to-End Processing Capability

6. 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 far-infrared spectral region, Appl.Opt. (accepted).

7. 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 IWCDm Models available at http://www.ssec.wisc.edu/~baum for IR Spectral Models (100 to 3250 cm-1) MODIS AVHRR AATSR MISR VIRS MAS (MODIS Airborne Simulator) ABI (Advanced Baseline Imager) POLDER (Polarization) SEVIRI (Spinning Enhanced Visible InfraRed Imager)

8. UW Hyperspectral Sounder Simulator & Processor (HSSP) Simulator - Radiance and Model Component

9. UW Hyperspectral Sounder Simulator & Processor (HSSP) Simulator - Radiance and Model Component • Effect/Feature IncludedNotes • Cloud Microphysics yes Measurements, NWP model output • Single Scattering Parameterization Partial ongoing effort • DISORT yes ongoing effort • Cloud Layer Albedo & Transmittance Par. Partial ongoing effort • Fast Cloudy RT Model Partial under development • Atmospheric profile data base yes • LBLRTM yes • Water Vapor Spectroscopy yes ongoing effort • Fast Clear RT Model yes PLOD • Adjoint operator yes MATLAB version • Tangent Linear yes MATLAB version • Ocean Surface Emissivity Model yes IRSSE Model (Van Delst) • Land Surface Emissivity Model not yet under development • Aerosol Parameterization not yet under development • Solar Spectrum not yet • RT Model validation partial ongoing effort • RT Model consolidation no coordination: PLOD; RTTOV; OPTRAN; OSS Mesoscale NWP MODEL yes MM5 and WRF • Improved Cloud Physics in NWP no cloud spectral bin modeling

10. Radiative transfer modeling of atmospheric gases absorption Surface Type • “ 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: • 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 Ozone Temp. Dust/Aerosol CO Temp. Water Vapor

11. Radiative transfer approximation of single cloud layer model

12. 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

13. 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).

14. 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

15. Database, 18 classes and 28 components [adapted from Levoni et al.,1997] describes aerosol physical-chemical properties using: Size Distribution: Lognormal distribution Modified gamma distribution Chemical composition: Complex refractive index Shape: Spherical ( Mie theory). We plan to extend the study by considering nonspherical particles Concentration: Any • Dependence of wavelengths • Hygroscopic particle, change with relative humidity • Internal mixture An Aerosol Database

16. UW Hyperspectral Sounder Simulator & Processor (HSSP) Simulator - Sensor Component • Effect/Feature IncludedNotes • Instrument Emission yes • Instrument Responsivity yes • Numerical Filter yes filter function set to unity • Instrument Phase yes varies linearly with n • Phase variation across FPA not yet • Off-axis OPD sampling yes • ILS variations yes • pixel-to-pixel offset variations yes* 12%(LW), 5%(SMW) random variation • pixel-to-pixel gain variations yes* 8-40%(LW), 2-5%(SMW) of full well depth • pixel operability not yet • FPA center not aligned with FTS axis yes 1-2 pixels, non integer • LW/SMW FPA misalignment no retrieval issue • Detector non-linearity no small • Detector noise yes • Photon noise yes* • Quantization noise yes* • OPD scan mirror velocity variation no small • OPD scan mirror tilt no small • Diffration blur no • Jitter blur no • Currently being implemented

17. UW Hyperspectral Sounder Simulator & Processor (HSSP) Processor - Measurement & Retrieval/Product Component • Effect/Feature IncludedNotes • Calibrated radiances yes generate sensor spectral measurements • Geo-location yes based on nominal geo orbit • Total sensor noise yes mainly random detector noise • Diffraction blur partial simulated to demonstrated band to band reg. Error effect • 4-km sampling yes MM5 meso-scale run • 15 to 30 minutes sampling yes MM5 meso-scale run • Clear radiances yes Latest PLOD fast clear model run • Cloudy radiances yes Water & Ice Clouds (includes size effect) • Aerosol/Dust radiances not yet Extinction modeling underdevelopment • Ocean emissivity yes IRSSE model • Land emissivity not yet underdevelopment (UH-UW) • Clear regression retrieval yes demonstrated by simulation, air/space borne • Clear physical retrieval yes developed under testing • Cloudy retrieval down to cloud level partial demonstrated by simulation and airborne • Cloudy retrieval – transparent clouds not yet under design • Altitude resolved water vapor wind yes demonstrated by simulation and airborne • 3D water vapor wind not yet under development • Cloud detection partial under development • Cloud clearing without microwave partial under development • Cloud property not yet under design • Lossless & Lossy data compression partial under development • Measurement Noise Estimation yes ongoing effort

18. AIRS Std. Operational Product CIMSS

19. 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

21. AIRS Absolute Emissivity Ozone Not Fit Atm. Corr. Relative IR Emiss Absolute IR Emiss • Squares are using 281 Select AIRS channels only. It Works !!!

22. 12 m Emissivity July 2003 MODIS AIRS AIRS - MODIS

23. 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)

25. 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.

26. 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 • MRF PBL • RRTM/Dudhia radiation • Explicit cumulus convection • OSU land surface model • WSM6 microphysics • YSU PBL • RRTM/Dudhia radiation • Explicit cumulus convection • NOAH land surface model

27. Global training database for hyperspectral and multi-spectral atmospheric retrievals Suzanne Wetzel Seemann, Eva Borbas Allen Huang, Jun Li, Paul Menzel

28. Non-dimensional Tb Sensitivity to Atmospheric Temperature (Thermal Source only) Clear sky Cloudy

29. Data Compression Demonstration

30. Ground Segment Processing Demonstration • GIPS Design Elements • Monitoring, Control, and Data Channels • Parallel Processing Pipeline Architecture • Modular Software Component Design

31. 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

32. 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

33. 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 pmMonday 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 * GOES-R partnerships.