AeroStat: Online Platform for the

Statistical Intercomparison of Aerosols

Gregory Leptoukh, NASA/GSFC (P.I.)Christopher Lynnes, NASA/GSFC (Co-I.)

Robert Levy, SSAI/GSFC (Co-I.)David Lary, U. of Texas at Dallas (Co-I.)

Peter Fox, RPI (Co-I.)Ralph Kahn , NASA/GSFC (Collaborator)

Lorraine Remer , NASA/GSFC (Collaborator)

Contributions fromM. Hegde, M. Petrenko, L. Petrov, J. Wei, R. Albayrak, K. Bryant, J. Amrhein, F. Fang, X. Hu, D. da Silva, S. Ahmad, S. Zednik, P.

West

Advancing Collaborative Connections for Earth System Science (ACCESS) Program

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Outline• Why AeroStat?• Data Fusion as a thread through AeroStat• DEMO (1, 2 & 3)

• AeroStat: Behind the Scene• AeroStat Status• AeroStat Plans

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Why AeroStat?• Different papers provide different views on whether MODIS and MISR

measure aerosols well.• Peer-reviewed papers usually are well behind the latest version of the data.• It is difficult to verify results of a published paper and resolve controversies

between different groups as it is difficult to reproduce the results - they might have dealt with either different data or used different quality controls or flags.

• It is important to have an online shareable environment where data processing and analysis can be done in a transparent way by any user of this environment and can be shared amongst all the members of the aerosol community.

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Sample scenario:Monitoring dust transport

• A single sensor measurement provides only limited coverage while using data from several sensors increase spatial coverage.

• Many aerosol scientists go to Giovanni where Level 3 gridded data from several sensors are already harmonized. They:• Explore MODIS data and plot time series of AOD over a

certain period of time and then zoom on the time period where AOD exhibits clear evidence of elevated aerosol loading.

• Run animation of AOD and pinpoint the exact days where dust was transported over Atlantic,

• Go to a Giovanni data fusion portal to look how different instrument saw the same dust transport.

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Kalashnikova & Kahn, 2008 Chin et al, in preparation

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AeroStat scenario flowExplore & Visualize Level 3

Compare Level 3

Correct Level 2

Compare Level 2Before and After

Merge Level 2 to new Level 3

Level 3 are too aggregated

Switch to high-res Level 2

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Explore & Visualize Level 2

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Providing up-to-date online environment for

aerosol studies (AeroStat).

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DEMO 1: point data

• http://giovanni.gsfc.nasa.gov/aerostat/

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CSV output for MODIS vs. MISR for 2007 over Izana

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Collaborative Environment

• Tag and categorize an interesting feature and/or anomaly in a plot• View marked-up features in plots related to the one currently

being viewed• Save bias calculation• Save fusion request settings (tag, comment, share a la Facebook)• Bug report tags• Provide user with list of tags (created by other users) for similar

datasets• Ability to re-run workflows from other user tags• Have a "My Contributions" option, where user can click on

previously tagged items, re-run workflow, view plots)

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AeroStat FlowMODISTerra

MISRTerra

Compute Coincidence

CoincidentMISR/MODIS

Correct Bias

Corrected Coincident

MISR/MODIS

Analyze Corrections

Correct Bias

Corrected MODIS

Corrected MISR

Correct Bias

Merge

Merged Data

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Offlin

eOnline

Onlin

e

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Types of Bias CorrectionType of Correction

Spatial Basis

Temporal Basis

Pros Cons

Relative (Cross-sensor) linear Climatological

Region Season Not influenced by data in other regions, good sampling

Difficult to validate

Relative (Cross-sensor) non-linear Climatological

Global Full data record

Complete sampling

Difficult to validate

Anchored Parameterized Linear

Near Aeronet stations

Full data record

Can be validated Limited areal sampling

Anchored Parameterized Non-Linear

Near Aeronet stations

Full data record

Can be validated Limited insight into correction

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Reuse or cross-useAeroStat reuses data/systems/ontology/approach

from:• MAPSS (ACCESS project: Charles Ichoku)• MDSA (ESTO project: Greg Leptoukh)• DQSS (ACCESS project: Chris Lynnes)• Agile Giovanni (GES-DISC: Lynnes & Leptoukh)• Starting: (ESDRERR project: Charles Ichoku)• Non-linear bias correction: David Lary, Arlindo

da Silva & Arif Albayrak2/18/2011

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AeroStat Recap

• Comparing aerosol data from different sensors is difficult and time consuming for users

• AeroStat provides an easy-to-use collaborative environment for exploring aerosol phenomena using multi-sensor data

• The result should be:• More transparency to colocation and comparison methods• More consistency in dealing with multi-sensor aerosol data • Easy sharing of results• With less user effort

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