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Satellite Data Us in PM Management: A Retrospective Assessment   Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th  Annual Conference and Exhibition June 22-27, Indianapolis, IN MexicanSmoke
Early Satellite Detection of Manmade Haze, 1976  Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B.  Mon. Weather Rev.   1976 SMS GOES June 30 1975
Scientific Challenge: Description of PM ,[object Object],[object Object],[object Object],Particulate matter is complex because of its multi-dimensionality It takes at leas 8 independent dimensions to describe the PM concentration pattern Dimension  Abbr. Data Sources Spatial dimensions X, Y Satellites, dense networks Height Z Lidar, soundings Time T Continuous monitoring Particle size D Size-segregated sampling Particle Composition C Speciated analysis Particle Shape/Form F Microscopy Ext/Internal Mixture M Microscopy
Technical Challenge: Characterization ,[object Object],[object Object],[object Object],[object Object],[object Object]
Vertical Pattern of Global Aerosol ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Just like the human eye, satellite sensors detect the total amount of solar radiation that is reflected from the earth’s surface ( R o ) and backscattered  by the atmosphere from aerosol, pure air, and clouds. A simplified expression for the relative radiatioin detected by a satellite sensor (I/I o ) is: I / I o  = R o  e -    + (1- e -  ) P Satellite Detection of Aerosols Today, geo-synchronous and polar orbiting satellites can detect different aspects of aerosols over the globe daily. where    is the aerosol optical thickness and P the angular light scattering probability.
 
Information ‘Refinery’ Value Chain  (Taylor, 1975) Organizing Grouping Classifying Formatting Displaying Analyzing Separating Evaluating Interpreting Synthesizing   Judging  Options  Quality Advantages Disadvantages Deciding  Matching goals, Compromising Bargaining  Deciding e.g. CIRA VIEWS e.g. Langley IDEA RAW System e.g. WG Summary Rpt e.g. RPO Manager Informing Knowledge Action Productive Knowledge Information Data
Asian Dust Cloud over N. America On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65   g/m 3 In Washington State, PM10 concentrations exceeded 100   g/m 3 Asian Dust 100   g/m 3 Hourly PM10
The Asian Dust Event of April 1998 On April 19, 1998 a major dust storm occurred over the Gobi Desert The dust cloud was seen by  SeaWiFS, TOMS, GMS, AVHRR satellites The transport of the dust cloud was followed on-line by an an ad-hoc international group China Mongolia Korea
Supporting Evidence: Transport Analysis Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and entering the continent.  The air masses arrive to Big Bend, TX form the east (July) and from the west (April)
Sahara PM10 Events over Eastern US ,[object Object],[object Object],Much previous work by Prospero, Cahill, Malm,  Scanning the AIRS PM10 and IMPROVE chemical databases several regional-scale PM10 episodes over the Gulf Coast (> 80 ug/m3) that can be attributed to Sahara. June 30, 1993 July 5, 1992 June 21 1997
May 15, 1998 ,[object Object],[object Object],Smoke from Central American Fires ,[object Object],[object Object]
July 2020 Quebec Smoke Event  ,[object Object],–   ,[object Object],[object Object]
May-June 2003 Siberian Fires
[object Object],May 9, 1998 A Really Bad Aerosol Day for N. America Asian Smoke C. American Smoke Canada Smoke
Global Oceanic Aerosol Pattern (1997) ,[object Object],Husar, Prospero, Stowe, 1997
Pattern of Fires over N. America ,[object Object],[object Object],Fire Pixel Count: Western US North America
GOES 8 ,[object Object],[object Object]
Retrieved Optical Depth 2000.08.15
Retrieved Optical Depth 2000.08.18
CAPITA NASA REASON Project: Application of Satellite Data to PM Management Schematic representation of data sharing in a federated information system. Based on the premise that providers expose part of their data (green) to others Data Federation Concept and the FASNET Network
Please Visit DataFed.Net
Web-based data delivery: Analysts Console
Near Real Time Public Satellite Data Delivery
Interactive Virtual Workgroup Websites July 2002 Quebec Smoke
R eal-Time  A erosol  W atch   (RAW) RAW is an open  communal facility to study non-industrial (e.g. dust and smoke) aerosol events , including detection, tracking and impact on PM and haze.  RAW output will be directly applicable, to  public health protection, Regional Haze rule, SIP and model development  as well as toward stimulating the scientific community.  The main asset of RAW is the  community of data analysts, modelers, managers  and others participating in the production of actionable knowledge from observations, models and human reasoning The RAW community will be supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools evolving under the umbrella of  Fast Aerosol Sensing Tools for Natural Event Tracking (FASTNET) . Initially, FASTNET is composed of the  Community Website   for open community interaction, the  Analysts Console  for diverse data access and the  Managers Console  for AQ management decision support.
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]

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2004-06-24 Satellite Data Us in PM Management: A Retrospective Assessment

  • 1. Satellite Data Us in PM Management: A Retrospective Assessment Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN MexicanSmoke
  • 2. Early Satellite Detection of Manmade Haze, 1976 Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B. Mon. Weather Rev. 1976 SMS GOES June 30 1975
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  • 6. Just like the human eye, satellite sensors detect the total amount of solar radiation that is reflected from the earth’s surface ( R o ) and backscattered by the atmosphere from aerosol, pure air, and clouds. A simplified expression for the relative radiatioin detected by a satellite sensor (I/I o ) is: I / I o = R o e -  + (1- e -  ) P Satellite Detection of Aerosols Today, geo-synchronous and polar orbiting satellites can detect different aspects of aerosols over the globe daily. where  is the aerosol optical thickness and P the angular light scattering probability.
  • 7.  
  • 8. Information ‘Refinery’ Value Chain (Taylor, 1975) Organizing Grouping Classifying Formatting Displaying Analyzing Separating Evaluating Interpreting Synthesizing Judging Options Quality Advantages Disadvantages Deciding Matching goals, Compromising Bargaining Deciding e.g. CIRA VIEWS e.g. Langley IDEA RAW System e.g. WG Summary Rpt e.g. RPO Manager Informing Knowledge Action Productive Knowledge Information Data
  • 9. Asian Dust Cloud over N. America On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65  g/m 3 In Washington State, PM10 concentrations exceeded 100  g/m 3 Asian Dust 100  g/m 3 Hourly PM10
  • 10. The Asian Dust Event of April 1998 On April 19, 1998 a major dust storm occurred over the Gobi Desert The dust cloud was seen by SeaWiFS, TOMS, GMS, AVHRR satellites The transport of the dust cloud was followed on-line by an an ad-hoc international group China Mongolia Korea
  • 11. Supporting Evidence: Transport Analysis Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and entering the continent. The air masses arrive to Big Bend, TX form the east (July) and from the west (April)
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  • 22. CAPITA NASA REASON Project: Application of Satellite Data to PM Management Schematic representation of data sharing in a federated information system. Based on the premise that providers expose part of their data (green) to others Data Federation Concept and the FASNET Network
  • 24. Web-based data delivery: Analysts Console
  • 25. Near Real Time Public Satellite Data Delivery
  • 26. Interactive Virtual Workgroup Websites July 2002 Quebec Smoke
  • 27. R eal-Time A erosol W atch (RAW) RAW is an open communal facility to study non-industrial (e.g. dust and smoke) aerosol events , including detection, tracking and impact on PM and haze. RAW output will be directly applicable, to public health protection, Regional Haze rule, SIP and model development as well as toward stimulating the scientific community. The main asset of RAW is the community of data analysts, modelers, managers and others participating in the production of actionable knowledge from observations, models and human reasoning The RAW community will be supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools evolving under the umbrella of Fast Aerosol Sensing Tools for Natural Event Tracking (FASTNET) . Initially, FASTNET is composed of the Community Website for open community interaction, the Analysts Console for diverse data access and the Managers Console for AQ management decision support.
  • 28.