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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Offl
ine
On
line
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 Albayrak
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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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