uw-cimss muri management & progress report wayne feltz (program manager)

4th Annual Workshop on Hyperspectal Meteorological Science of UW MURI, GIFTS,and GOES-R27-28 April 2004, Madison, Wisconsin

UW-CIMSS MURI ManagementUW-CIMSS MURI Management & Progress Report

Wayne Feltz(Program Manager)

• MURI Research Update

• UW & UH Co-Investigator Interactions• Research Timeline for UW–MURI• Overall Status• Progress Reports: The UW & other Co-Is


Hyperspectral WBS & Personnel AllocationHyperspectral WBS & Personnel Allocation

MURI

Clouds & Cloud Modeling

RetrievalAlgorithms

Ocean Emiss.Modeling

ForwardModeling

PBL Winds

NumericalModeling

Land SurfaceModeling

Stability &Turbulence

SurfaceCharacterization

Dust &Visibility

Allen Huang (PI)Wayne F. Feltz (PM)

Jun LiWang Xuanji

Dave TobinLeslie Moy, Jim Davies

Steve AckermanR. Dengel, D. Stettner

Chris Velden, B. HuangWayne FeltzKris Bedka

Paul van Delst Jason Otkin

Ping Yang(UT A&M)

G. Jedlovec(UAH)

Paul Lucey(UH-HIGH)

Robert KnutesonSuzanne SeemanEva Borbas

UW-CIMSS Support Staff: Hal Woolf, Elizabeth Weise, Erik Olson, Dave Santek, Kevin Baggett, Tom Rink, Tom Whittaker

Students: Ryan Aschbrenner Benjamin Johnson seeking others

1st Order

2nd Order


MURI HighlightsMURI Highlights

• 1st Phase (Years 1-3) Reviewed and Optional 2nd Phase (Year 4-5) granted funding

• Basic research will be honed for hyperspectral meteorological applications during Years 4-5

• Leveraging with other hyperspectral funding (GOES-R Risk Reduction) to support general Navy, NOAA, and NASA hyperspectral science

• More than 20 conference papers and 6 journal papers published with MURI related efforts


Tasks for UW-MURITasks for UW-MURI

1 Mathematical Quantification of Useful Hyperspectral

Information 2 Radiative Transfer Modeling

• Clear and Cloudy Sky Emission/Absorption• Atmospheric Particulate Emission/Absorption• Surface Emission/Absorption• Adjoint & Linear Tangent

3 Mathematical Retrieval Algorithm Development• Atmospheric Parameters• Suspended Particulate Detection and Quantification• Sea Surface Temperature• Surface Material Identification

4 Product Research• Ocean and Land Surface Characterization• Lower Tropospheric Temperature, Moisture and Winds• Surface Material Products• Aerosols• Derived (Second Order) Products• Visibility

New Additions (in Red)


Progress Reports: The UW & UH Co-IsProgress Reports: The UW & UH Co-Is

• NWP in support of Simulating Hyperspectral data• Information Content• “Clear Sky” RTE Development• Hyperspectral Clear/Cloudy Retrievals

• Simulated• Measurements

• Derived Winds & Atmospheric Parameters; SST

UW–CIMSS:

UH–HIGP:• AHI field experiments & hyperspectral modeling• Surface Emissivity


New ScienceApplications

Science Use of GIFTS Data:Science Use of GIFTS Data:Where do we stand today?Where do we stand today?

SimulatingGIFTS data

Fast Modeldevelopment

InformationContent

First Retrievals(T, q, wind)

Convection &Stability

DataAssimilation

Studies

EstimatingVisibility

CloudProperties

Dust & AerosolRetrievals

0 50 95

6.26.4

EstimatingTurbulence

UW-MURI

Y0 Y1 Y2 Y3-Y5Y3-Y5 Y4-Y5


Forward Modeling WorkForward Modeling Work“ 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


Cloudmicrophysics

(water sphere /Ice crystal habit,

ref. ind., size dist.)

Atmosphericprofiles for training

(P,T,H2O,O3)

Surfaceparameters

(emissivity: UW-CIMSS,UH-HIGP, UCSB)

Aerosol parametersfor important species *

(Ref. ind., size dist.:I. Sokolik -> ω0, : g Mie)

Solar spectrum at GIFTS

*instrument bands

( )Irradiance

Single scattering parameter computation

( : water sphereMie, < 100 ice μ : m T- ,matrix > 100 ice μ : /m GOMMie)

Single scatteringparameters

(Qe, Qa, )g

& LBLRTM correlatedk-distribution

computation for GIFTS bands

Coefficients for computing TOA to layer

clear sky optical depths

( , , 2 , 3)as FN of P T H O O

DISORT radiative transfer for cloud

particles only

Cloud layer opticalproperties

(albedo, )transmittance

Clear sky optical depthparameter

computation

Pressure layer ( )optical depth PLOD

( TOA to layer clear )sky optical depths

Cloud layer albedoandtransmittance

parameterization

Coefficients for computing cloudalbedo

and transmittance

( ,as FN of ODeff. ,size zenithθ)

Case to simulate

Atmospheric profile( , , 2 , 3)P T H O O

Cloud configuration( , ,Z OD eff. , / )size ice liquid

Aerosol configuration( , , ) *Z AOD species

GIFTS fast RT model

: Clear sky: 6 / (Speed s full spectrum cf 4 )m with LBLRTM

: 0.1 Accuracy approx K cf LBLRTM

:Cloudy sky: 7 / (Speed s full spectrumcf 7 )m with LBLDIS

: 0.5 Accuracy approx K cf LBLDIS

Radiance spectrum

Tangent linear

Adjoint operator

Gas component optical depth

* Not implemented &In collaboration with Texas A M . In collaboration with ISokolik In collaboration with U Hawaii

MURI Hyperspectral Cloud/Aerosol/Haze Radiative Transfer Modeling


Completed a high-resolution MM5 simulation of an intense upper-tropospheric jet streak that occurred over the north-central Pacific during the 2003 THORPEX field study. This simulated atmosphere was passed through the GIFTS forward model to obtain top of atmosphere radiances.

GIFTS Spectrum: Clear Sky

Installed and successfully performed several multiple-processor Weather Research and Forecasting (WRF) simulations. Ongoing work involves comparing WRF and MM5 model output to determine the ability of the WRF model to simulate the fine-scale water structure during a convective initiation event.

MURI Hyperspectral Modeling Activities


• Clear sounding retrieval –Regression and–Regularization (physical retrieval)

• Cloudy Sounding retrieval–Hole hunting for single FOV–Cloud clearing using imager/sounder (ABI/HES, MODIS/AIRS are be used for testing the algorithm)–Cloudy regression

Hyperspectral Clear/Cloudy Sounding Retrieval


1. Cube study

2. MODIS/AIRS demonstration(IHOP, June 12, 2002)

AIRS Single FOV RTV vs. ECMWF Analysis : TPW 09-02-2003, granule 192, daytime


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

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

Hyperspectral Winds Retrieval


Hyperspectral NAST-I/S-HIS RetrievalsPTOST and ATOST Campaigns

Scanning HIS Relative Humidity and Cloud Phase Lidar cloud boundaries

MODIS Airborne Simulator


UW-CIMSS Satellite Convective Storm UW-CIMSS Satellite Convective Storm NowcastingNowcasting

GOES-12 1 km Visible and 4 km Imager: 4 May 2003

Convective Cloud Mask

Nowcast Time

Multi-spectral Techniques

Cloud-top Cooling Estimates Using Satellite-Derived

Winds Identify pre-CI signatures in GOES Visible and IR data using: 1) convective cloud masking

2) multi-spectral band differencing techniques 3) cloud-top temperature trend assessments

Develop CI nowcasts (0-1 hour) by accumulating pre-CI satellite indicators attributed to the first occurrence of a ≥ 30 dBZ radar echo

Incorporate Satellite-Based Convective Cloud Analyses for Nowcasting Convective

Initiation (CI)

CI Nowcast Algorithm

Red: CI Nowcasts Grey: Cirrus Anvil

Doppler Radar for Validation

1 Hour Later


Land Surface Temperature from AIRSLand Surface Temperature from AIRS

DOE Southern Great Plains ARM site temperature contrast.

B.T. (K)

UWOnline/OfflineTs (K)


UW-Navy Hardware Investments*

UW HSSP Parallel Computing System

Objective: In support of a government/industry Broad Scope of

Hyperspectral Activities

SystemGeneral

Specification $

Computing Cluster

72 AMD Opteron CPU (64 bits)

16 SGI Itanium CPU (64 bits)

$360K

Server & Storage

48 Terabytes $100K

Grand Total$460K

*ONR DURIP Award


STATUSSTATUS

• The UW & UH MURI is a healthy research collaboration which includes two other external investigators

• We are through our proposed Year 3 and have been granted funding for two optional years of MURI work.

• We are progressing on many fronts, from RTE and toward atmospheric/surface applications development

• We are using the simulated GIFTS and operational AIRS/NAST-I/S-HIS data and and our rapidly implementing the new knowledge gained through this grant towards future hyperspectral applications

• More investigator details to follow


Today1:452:05 pm Clear Forward Models & linear operators L. Moy2:052:25 pm Cloudy Fast Forward Model J. Davies2:252:45 pm Simulated Profiles Retrievals: Clear & Cloudy J. Li4:505:30 pm IMAPP AIRS Sounding Demonstration Baggett/Weisz

Tomorrow8:30-8:50 am Multiple Spectral and Hyperspectral Wind Demonstration Velden/Huang8:50-9:10 am Demonstration of 3D Water Vapor Tracked Winds B. Huang9:10-9:30 am Training database for Hyperspectral …Applications S. Seemann9:30-9:50 am Hyperspectral cloud boundary retrieval R. Holz

10:10-10:30 am Hyperspectral Land Surface Modeling & Retrieval R. Knuteson10:30-10:50 am Infrared Sea Surface Emissivity (IRSSE) Model P. Van Delst10:50-11:10 am Hyperspectral Applications for Aviation W. Feltz11:10-11:30 am Automatic GOES Nowcasting of Convective Initiation K. Bedka11:30-12:00 pm Hyperspectral Signature Survey: Fire Plumes, .. Clouds D. Tobin1:00-1:45 pm UW Hyperspectral Sounder Simulator & Processor A. Huang2:30-3:20 pm Hyperspectral Visualization Demonstration Rink/Tobin

SSEC MURI PresentationsSSEC MURI Presentations

uw-cimss muri management & progress report wayne feltz (program manager)

Documents

hyperspectral funding

nasa hyperspectral science

funding basic research

phase years

modeling work lblrtm

muri related effortstasks

particulate detection

tom whittakerstudents