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AeroStat: Online Platform for the Statistical Intercomparison of Aerosols

Advancing Collaborative Connections for Earth System Science (ACCESS) Program. 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.)

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AeroStat: Online Platform for the Statistical Intercomparison of Aerosols

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  1. Advancing Collaborative Connections for Earth System Science (ACCESS) Program 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 from M. 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

  2. Outline Why AeroStat? Data Fusion as a thread through AeroStat DEMO (1, 2 & 3) AeroStat: Behind the Scene AeroStat Status AeroStat Plans

  3. 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.

  4. Sample scenario:Monitoring dust transport Kalashnikova & Kahn, 2008 Chin et al, in preparation • 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.

  5. AeroStat scenario flow Explore & Visualize Level 3 Compare Level 3 Explore & Visualize Level 2 Correct Level 2 Level 3 are too aggregated Switch to high-res Level 2 Compare Level 2 Before and After Merge Level 2 to new Level 3

  6. Providing up-to-date online environment for aerosol studies (AeroStat).

  7. DEMO 1: point data http://giovanni.gsfc.nasa.gov/aerostat/

  8. CSV output for MODIS vs. MISR for 2007 over Izana

  9. 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)

  10. AeroStat Flow MODIS Terra MISR Terra Correct Bias Correct Bias Offline Compute Coincidence Corrected MISR Corrected MODIS Online Coincident MISR/MODIS Online Merge Correct Bias Corrected Coincident MISR/MODIS Merged Data Analyze Corrections

  11. Types of Bias Correction

  12. Reuse or cross-use AeroStat 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: (ESDRERRproject: Charles Ichoku) Non-linear bias correction: David Lary, Arlindo da Silva & Arif Albayrak

  13. 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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