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Collaborative Integration of Satellite and Surface Data for Characterization of Aerosol Events. E. M. Robinson Advisor, R. B. Husar 2010 M.S. Thesis St. Louis, MO, Nov. 3, 2010. Satellite-Integral. Illustrate the use of multi-sensory data. Technical Challenge: Characterization

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Collaborative Integration of Satellite and Surface Data for Characterization of Aerosol Events


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collaborative integration of satellite and surface data for characterization of aerosol events

Collaborative Integration of Satellite and Surface Data for Characterization of Aerosol Events

E. M. Robinson

Advisor, R. B. Husar

2010 M.S. Thesis

St. Louis, MO, Nov. 3, 2010

illustrate the use of multi sensory data

Satellite-Integral

Illustrate the use of multi-sensory data

Technical Challenge: Characterization

  • PM characterization requires many sensors, sampling methods and analysis tools
  • Each sensor/method covers only a fraction of the 7-Dimensional PM data space.
    • Spatial dimensions (X, Y, Z)
    • Temporal Dimensions (T)
    • Particle size (D)
    • Particle Composition ( C )
    • Particle Shape (S)
  • Most of the 7 Dim PM data space is extrapolated from sparse measured data
  • Others sensors integrate over time, space, chemistry, size etc. .

Satellites, have high spatial resolution but integrate over height H, size D, composition C, particle shape

kansas agricultural smoke april 12 2003
Kansas Agricultural Smoke, April 12, 2003

Organics

35 ug/m3 max

Fire Pixels

PM25 Mass, FRM

65 ug/m3 max

Ag Fires

SeaWiFS, Refl

SeaWiFS, AOT Col

AOT Blue

hurdles
Hurdles

“The user cannot find the data;

If he can find it, cannot access it;

If he can access it, ;

he doesn't know how good they are;

if he finds them good, he can not merge them with other data”

The Users View of IT, NAS 1989

To overcome the first two hurdles need:

  • Service oriented architecture
  • Standards for finding and accessing the data
  • Open, collaborative space to coordinate work
soa actions
SOA Actions

Actions:Register– Discover-Access

User

Provider

Broker

The data reuse is possible through the service oriented architecture of GEOSS.

soa actions1
SOA Actions

Actions:Register– Discover-Access

Broker

User

Provider

The data reuse is possible through the service oriented architecture of GEOSS.

preparation of data and metadata
Preparation of Data and Metadata

GEOSS

Clearinghouse

Access

Provider

User

Data Protocol

WMS, WCS (netDCF CF) + conventions

Data

Metadata

preparation of data and metadata1
Preparation of Data and Metadata

GEOSS

Clearinghouse

Register Metadata

Provider

User

Data Protocol

WMS, WCS (netDCF CF) + conventions

Metadata

ISO 19115 subset for Geospatial Data

Data

Metadata

air quality metadata record
Air Quality Metadata Record

GEOSS

Clearinghouse

Discover, Get Access Key

Provider

User

Metadata for Finding and Accessing Data

Data Binding

Air Quality

Specific

OGC CSW

Queryable

OGC CSW

Returnable

Metadata

Description

ISO 19115CSW Profile

slide10
Exceptional Event Rule:An air quality exceedance that would not have occurred but for the presence of a natural event.

Transported Pollution

Transported African, Asian Dust; Smoke from Mexican fires & Mining dust, Ag. Emissions

Natural Events

Nat. Disasters.; High Wind Events; Wild land Fires; Stratospheric Ozone; Prescribed Fires

Human Activities

Chemical Spills; Industrial Accidents; July 4th; Structural Fires; Terrorist Attack

Satellite remote sensors provide key observations for Exceptional Events

may 2007 georgia fires an actual exceptional event analysis for epa
May 2007 Georgia FiresAn actual Exceptional Event Analysis for EPA

May 5, 2007

May 12, 2007

Observations Used:

omi no2 quantifies the no2 emission
OMI NO2 Quantifies the NO2 Emission

Sweat Water fire in S. Georgia (May 2007)

3 evidence aerosol composition
3. Evidence: Aerosol Composition

Sulfate

Organics

Measured

Sulfate

Organics

Modeled

soa actions2
SOA Actions

Actions:Register– Discover-Access

Broker

User

Provider

The data reuse is possible through the service oriented architecture of GEOSS.

social media listening for air quality
Social Media Listening for Air Quality

RSS Feeds

Air Twitter Aggregator

Air Twitter Filter

ESIPAQWG

air twitter event identification
Air Twitter – Event Identification

August 2009, Los Angeles Fires

Normal Weekly Trend

air quality eventspaces
Air Quality EventSpaces

EventSpaces are community workspaces on the ESIP wiki that are created to describe the Event

Science Data

Social Media