1 navy’s muri impact on uw hyperspectral activities allen huang cooperative institute for...

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Navy’s MURI Impact on UW Hyperspectral Activities

Allen HuangCooperative 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 WisconsinMadison702 Langdon Street, Madison (608-262-1122)

7-9 June 2005

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UW’s road to the Hyperspectral (Next Generation) Sounders

VAS (12; GEO; O)

GOES Sounder (18; GEO; O)

GIFTS (~1600; GEO; E)

HES (~1600; GEO; O)

Time

(# of spectral bands)O: Operational

E: ExperimentalVTPR, HIRS (18; LEO; O)

CrIS (~2215; LEO; O)

IASI (~8000; LEO; O)

AIRS (~2200; LEO; E)

HIS (4492; Airborne)

IRIS (862; LEO; E)

IMG (18400; LEO; E)

NAST-I (8220; Airborne)

UW has played a significant roles in the

past, current, and future Hyperspectral Sounders

(labeled in green)

S-HIS (4840; Airborne)

1978 2012

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UW’S Hyperspectral End-to-End Simulation Effort

Mesoscale Modeling

ProfilesClouds

Surface tempWind

Radiative Transfer Modeling

Top of Atmosphereradiances

FTS Simulator

Interferograms

Compression

Calibration

CompressedData (Rad. &Counts)

Spectra Normalized INFGs

Off-AxisNormalization

Profile Tracking

Wind

Instrument DesignCompression Impacts

Trade Study

Retrieval

Profiles

Val

idat

ion

: Outputs

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Current UW Direct Broadcast End-to-End Processing Capability

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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).

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Bulk Scattering ModelsAvailable for Multiple Instruments

Bulk Scattering ModelsAvailable 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

IWC Dm

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)

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UW Hyperspectral Sounder Simulator & Processor (HSSP)

Simulator - Radiance and Model Component

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Effect/Feature Included Notes•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


Simulator - Radiance and Model Component

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“ 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

Temp. OzoneSurface

Type

Water Vapor

Dust/Aerosol Temp.CO

Radiative transfer modeling of atmospheric gases absorption

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Radiative transfer approximation of single cloud layer model

1

2

3

4

5

6

7

98

99

100

101

Pc

1100

0

Ps

Layer#

Pressure (hPa)

1

c

s

I

s

c

0

gaseoustrans. / OD

II

I

I

I RRc

I c

Icc

I I0 c + Icc + I + I RRc

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3 ice cloud models, 1 water cloud model100-3246 1/cm (~3-100 um)

Water-spheresDe = 2-1100 um

TropicalDe = 16-126 um

Mid-latitudeDe = 8-145 um

PolarDe = 1.6-162 um

Two layer cloud model from Texas A&M coupled with UW/CIMSS clear-sky model

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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).

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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

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An Aerosol Database

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

dN(r)dr

Nr ln10 2 log

exp[ (logr r0 )2

2(log )2]

dN(r)dr

Nr exp( br )

Dependence of wavelengths Hygroscopic particle, change with relative humidity Internal mixture

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Simulator - Sensor Component Effect/Feature Included Notes•Instrument Emission yes•Instrument Responsivity yes•Numerical Filter yes filter function set to unity•Instrument Phase yes varies linearly with •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

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Processor - Measurement & Retrieval/Product Component Effect/Feature Included Notes•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

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AIRS Std. Operational Product

CIMSS

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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. - BlackSingle-Ch.:Band 31 – GreenBand 22 - Red

South Africa Granule

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AbsoluteIR Emiss

• Squares are using 281 Select AIRS channels only. It Works !!!

AIRS Absolute Emissivity

OzoneNot Fit

Atm. Corr.RelativeIR Emiss

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July 200312 m Emissivity

MODIS AIRS

AIRS - MODIS

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Simulated GIFTS winds (left) versus GOES current oper winds (right)

GIFTS - IHOP simulation 1830z 12 June 02 GOES-8 winds 1655z 12 June 02

Altitude Resolved Water Vapor Wind Demonstration

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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.

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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

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Global training database for hyperspectral and multi-spectral atmospheric retrievals

Suzanne Wetzel Seemann, Eva BorbasAllen Huang, Jun Li, Paul Menzel

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Non-dimensional Tb Sensitivity to Atmospheric Temperature

(Thermal Source only)

Clear sky

Cloudy

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Data Compression Demonstration

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Ground Segment Processing Demonstration

GIPS Design Elements• Monitoring, Control, and Data Channels• Parallel Processing Pipeline Architecture• Modular Software Component Design

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Itemized ImpactsPhysical 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

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Overall ImpactOverall Impact

Every Element of a Truly End-To-End Every Element of a Truly End-To-End Infrastructure Under Construction at Infrastructure Under Construction at

SSEC/CIMSS of UW-Madison in Support SSEC/CIMSS of UW-Madison in Support of NPP/NPOESS & GOES-R Activities of NPP/NPOESS & GOES-R Activities

Through Through Three-Pillar PartnershipThree-Pillar Partnership

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Monday-Thursday 1-4 August 2005Atmospheric and Environmental Remote Sensing Data

Processing and Utilization: Numerical Atmospheric Prediction and Environmental Monitoring

3:30 to 5:30 pm Monday 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 PerspectiveIndustry PerspectiveAcademia PerspectiveNational 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

1 navy’s muri impact on uw hyperspectral activities allen huang cooperative institute for...

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