1. 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 Advancing Collaborative Connections for Earth System Science (ACCESS) Program 2/18/2011

2. Outline Why AeroStat? Data Fusion as a thread through AeroStat DEMO (1, 2 & 3) AeroStat: Behind the Scene AeroStat Status AeroStat Plans 2/18/20112

3. Why AeroStat?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. 2/18/20113

4. 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. 2/18/2011 Kalashnikova & Kahn, 2008 Chin et al, in preparation 4

5. AeroStat scenario flowAeroStat scenario flow Explore & Visualize Level 3 Compare Level 3 Correct Level 2 Compare Level 2 Before and After Merge Level 2 to new Level 3 Level 3 are too aggregated Switch to high-res Level 2 2/18/2011 Explore & Visualize Level 2 5

6. Providing up-to-date online environment for aerosol studies (AeroStat). 2/18/20116

7. DEMO 1: point dataDEMO 1: point data http://giovanni.gsfc.nasa.gov/aerostat/ 2/18/20117

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13. 2/18/2011 CSV output for MODIS vs. MISR for 2007 over IzanaCSV output for MODIS vs. MISR for 2007 over Izana 13

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15. Collaborative EnvironmentCollaborative 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) 2/18/201115

16. AeroStat FlowAeroStat Flow MODIS Terra MISR Terra Compute Coincidence Coincident MISR/MODIS Correct Bias Corrected Coincident MISR/MODIS Analyze Corrections Correct Bias Corrected MODIS Corrected MISR Correct Bias Merge Merged Data 2/18/2011 Offline Online 16 Online

17. Types of Bias CorrectionTypes of Bias Correction Type of Correction Spatial Basis Temporal Basis ProsCons Relative (Cross- sensor) linear Climatological RegionSeasonNot influenced by data in other regions, good sampling Difficult to validate Relative (Cross- sensor) non- linear Climatological GlobalFull data record Complete samplingDifficult to validate Anchored Parameterized Linear Near Aeronet stations Full data record Can be validatedLimited areal sampling Anchored Parameterized Non-Linear Near Aeronet stations Full data record Can be validatedLimited insight into correction 2/18/201117

18. Reuse or cross-useReuse 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: (ESDRERR project: Charles Ichoku) Non-linear bias correction: David Lary, Arlindo da Silva & Arif Albayrak 2/18/201118

19. AeroStat RecapAeroStat 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 2/18/201119