Smoke Characterization from MODIS Imagery Buck Sharpton Kevin Engle GINA/ION University of Alaska Fairbanks Buck Sharpton Kevin Engle GINA/ION University.

Slides:



Advertisements
Similar presentations
Lisl Robertson, University of Cape Town, RSA with assistance from Stewart Bernard Christo Whittle GOOS AFRICA.
Advertisements

GEMS-Aerosol WP_AER_4: Evaluation of the model and analysis Lead Partners: NUIG & CNRS-LOA Partners: DWD, RMIB, MPI-M, CEA- IPSL-LSCE,ECMWF, DLR (at no.
Deep Blue Algorithm: Retrieval of Aerosol Optical Depth using MODIS data obtained over bright surfaces 1.Example from the Saharan Desert. 2.Deep Blue Algorithm.
Canadian Wildland Fire Information System Natural Resources Canada Canadian Forest Service Ressources naturelles Canada Service canadian des forêts.
This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 1 PM MAPPER®: An air.
OTHER SATELLITE DATASETS 1.Ocean Biology 2.Vegetation Cover 3.Fire Counts.
Canadian Wildland Fire Information System Natural Resources Canada Canadian Forest Service Ressources naturelles Canada Service canadian des forêts.
A Tutorial on MODIS and VIIRS Aerosol Products from Direct Broadcast Data on IDEA Hai Zhang 1, Shobha Kondragunta 2, Hongqing Liu 1 1.IMSG at NOAA 2.NOAA.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Air Quality Products from.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Measurement of the Aerosol Optical Depth in Moscow city, Russia during the wildfire in summer 2010 DAMBAR AIR.
Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
An Overview of Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Originally presented as part.
IDEA - Infusing satellite Data into Environmental (air quality) Applications Summer 2003: Prototype Analysis, Fusion, and Visualization of NASA ESE and.
Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),
Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
1 FIRE DETECTION BY SATELLITE FOR FIRE CONTROL IN MONGOLIA Global Geostationary Fire Monitoring Workshop on March, 2004 Darmstadt Germany S.Tuya,
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
VALIDATION OF SUOMI NPP/VIIRS OPERATIONAL AEROSOL PRODUCTS THROUGH MULTI-SENSOR INTERCOMPARISONS Huang, J. I. Laszlo, S. Kondragunta,
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Trajectory validation using tracers of opportunity such as fire plumes and dust episodes Narendra Adhikari March 26, 2007 ATMS790 Seminar (Dr. Pat Arnott)
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
1 AOD to PM2.5 to AQC – An excel sheet exercise ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan Gupta Salt.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
Surface UV from TOMS/OMI measurements N. Krotkov 1, J. Herman 2, P.K. Bhartia 2, A. Tanskanen 3, A. Arola 4 1.Goddard Earth Sciences and Technology (GEST)
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET-AQ Applied Remote SEnsing Training A project of NASA Applied Sciences Pawan Gupta Originally.
1 GOES-R Air Quality Proving Ground Leads: UAH UMBC NESDIS/STAR.
Significant contributions from: Todd Schaack and Allen Lenzen (UW-Madison, Space Science and Engineering Center) Mark C. Green (Desert Research Institute)
Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),
Causes of Haze Assessment Update for Fire Emissions Joint Forum -12/9/04 Meeting Marc Pitchford.
Air Quality Applications of NOAA Operational Satellite Data S. Kondragunta NOAA/NESDIS Center for Satellite Applications and Research.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Figure 2. Trend of chemical b ext at (a) BLIS1 and (b) SOLA1. The whiskers and boxes indicate 90 th, 80 th, 20 th, and 10 th percentile of b ext for each.
Overview of Fire Occurence Accuracy Assessment Wilfrid Schroeder PROARCO – IBAMA University of Maryland – Dept of Geography Ground-based Accuracy Assessment.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
Managing Smoke and Emissions. A new system for managing smoke and emissions in Victoria that will provide for coordinated: Investment Service delivery.
1 AOD to PM2.5 to AQC – An excel sheet exercise ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan Gupta NASA.
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Introduction 1. Advantages and difficulties related to the use of optical data 2. Aerosol retrieval and comparison methodology 3. Results of the comparison.
Satellite Basics MAC Smog Blog Training CATHALAC, Panama, Sept 11-12, 2008 Jill Engel-Cox & Erica Zell Battelle Memorial Institute
Air and Waste Management Association Professional Development Course AIR-257: Satellite Detection of Aerosols Issues and Opportunities Fraction.
April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.
Aerosol Characterization Using the SeaWiFS Sensor and Surface Data E. M. Robinson and R. B. Husar Washington University, St. Louis, MO
Earth Observing Satellites Update John Murray, NASA Langley Research Center NASA Aviation Weather Satellites Last Year NASA’s AURA satellite, the chemistry.
Analysis of Simultaneous Nadir Observations of MODIS from AQUA and TERRA Andrew Heidinger and many others NOAA/NESDIS Office of Research and Applications.
Satellites Model Validation Parameterizations Parameterizations Climate Sensitivity Climate Sensitivity Underlying mechanisms Underlying mechanisms CURRENT.
Aerosol Pattern over Southern North America Tropospheric Aerosols: Science and Decisions in an International Community A NARSTO Technical Symposium on.
North American Visibility. rdyswth Seasonal Bext.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
Preliminary Analysis of Relative MODIS Terra-Aqua Calibration Over Solar Village and Railroad Valley Sites Using ASRVN A. Lyapustin, Y. Wang, X. Xiong,
USDA Forest Service, Remote Sensing Applications Center, FSWeb: WWW: National Geospatial Fire.
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Fire Products NASA ARSET-AQ Links Updated November 2013 ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences.
MODIS Atmosphere Group Summary Collection 5 Status Collection 5 Status  Summary of modifications and enhancements in collection 5 (mostly covered in posters)
Fire, Smoke & Air Quality: Tools for Data Exploration & Analysis : Data Sharing/Processing Infrastructure This project integrates.
Eun-Su Yang and Sundar A. Christopher Earth System Science Center University of Alabama in Huntsville Shobha Kondragunta NOAA/NESDIS Improving Air Quality.
PM 2.5 Transport From Wildfires Case Study: Bugaboo Fire – Georgia/Florida, May 2007 Sean Ryan.
Remote Sensing of the Ocean and Coastal Waters
Jian Wang, Ph.D IMCS Rutgers University
NOAA GOES-R Air Quality Proving Ground Case Study 2 May 24, 2007
NOAA GOES-R Air Quality Proving Ground July 4, 2012 Case Study
McIDAS-X Software Development and Demonstration
Presentation transcript:

Smoke Characterization from MODIS Imagery Buck Sharpton Kevin Engle GINA/ION University of Alaska Fairbanks Buck Sharpton Kevin Engle GINA/ION University of Alaska Fairbanks

ION Node Focus on real-time satellite reception – many passes per dayFocus on real-time satellite reception – many passes per day Polar-orbiting satellitesPolar-orbiting satellites –NOAA AVHRR –MODIS (Terra & Aqua) –SeaWiFS* –GLI (ADEOS II) Basic and custom product generation and distributionBasic and custom product generation and distribution AmericaViewAmericaView Focus on real-time satellite reception – many passes per dayFocus on real-time satellite reception – many passes per day Polar-orbiting satellitesPolar-orbiting satellites –NOAA AVHRR –MODIS (Terra & Aqua) –SeaWiFS* –GLI (ADEOS II) Basic and custom product generation and distributionBasic and custom product generation and distribution AmericaViewAmericaView * Distributed by NASA/GSFC

Early Detection and Characterization of Boreal Wildfires Operational:Operational: –Supply Fire Detection/Management Products to Alaska Fire Service, Tanana Chiefs –Developing Smoke Characterization Products (Smoke Health Index) Research:Research: –Smoke properties (fuel, fire, environment) –Carbon cycling Operational:Operational: –Supply Fire Detection/Management Products to Alaska Fire Service, Tanana Chiefs –Developing Smoke Characterization Products (Smoke Health Index) Research:Research: –Smoke properties (fuel, fire, environment) –Carbon cycling

Smoke Characterization: Approach Refine & Extend MODIS AOT AlgorithmRefine & Extend MODIS AOT Algorithm –For use over bright, variable surfaces –To estimate smoke density, smoke height, particle size distribution. Validate using field/laboratory instrumentsValidate using field/laboratory instruments Generate Smoke-Health Index MapGenerate Smoke-Health Index Map –Translate smoke concentration into health risk –Add information on particulates and associated gases and aerosols of concern –Updateable and web-deliverable Refine & Extend MODIS AOT AlgorithmRefine & Extend MODIS AOT Algorithm –For use over bright, variable surfaces –To estimate smoke density, smoke height, particle size distribution. Validate using field/laboratory instrumentsValidate using field/laboratory instruments Generate Smoke-Health Index MapGenerate Smoke-Health Index Map –Translate smoke concentration into health risk –Add information on particulates and associated gases and aerosols of concern –Updateable and web-deliverable

Smoke Characterization: Advantages Real-Time MODIS, AVHRR, GLI receptionReal-Time MODIS, AVHRR, GLI reception –Up to 16 passes over station per day (Aqua, Terra) –Identical viewing geometry daily per instrument. An active fire season each yearAn active fire season each year Partnerships with Fire Management AgenciesPartnerships with Fire Management Agencies ExpertiseExpertise Field sites and Laboratory EquipmentField sites and Laboratory Equipment Real-Time MODIS, AVHRR, GLI receptionReal-Time MODIS, AVHRR, GLI reception –Up to 16 passes over station per day (Aqua, Terra) –Identical viewing geometry daily per instrument. An active fire season each yearAn active fire season each year Partnerships with Fire Management AgenciesPartnerships with Fire Management Agencies ExpertiseExpertise Field sites and Laboratory EquipmentField sites and Laboratory Equipment

MODIS Smoke Scene Clear Sky Scene Band math (normalization) Scene with surface reflectances removed Aerosol reflectances mixed with surface reflectances Surface reflectances only Yields Aerosol Optical Thickness Health-Risk Scene Based on translation schema in wide use by Health Officials Convert AOT to Smoke Density Smoke Air Quality Index

Steps: Test, refine MODIS AOTTest, refine MODIS AOT Field/laboratory measurementsField/laboratory measurements Theoretical approachesTheoretical approaches Convert AOT to Health RiskConvert AOT to Health Risk ValidateValidate Field monitoringField monitoring Client feedbackClient feedback DeliverDeliver Updateable several times dailyUpdateable several times daily Deliverable through InternetDeliverable through Internet Graphical, flexible, usefulGraphical, flexible, useful Smoke Air Quality Index