1. 1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number 07418004) PhD Advisor Professor Virendra Sethi February 9, 2010 Second Annual Progress Report

2. 2 of 26 A Brief Overview of First Annual Progress Report a critical review of literature was conducted on major application areas of satellite remote sensing for air quality  Improving retrieval algorithms  Studies to correlate observations to ground measurement  Air quality forecasting through satellite remote sensing  Identification and assessment of regional or continental air pollution hot spots and long range transport of pollution  Estimation of surface emissions Analysis of PM 10 over Mumbai using Satellite data and Ground Measurements

3. 3 of 26 Work accomplished last year Problem formulation and defining objectives Literature review in the area of formulated problem Initial studies with Geostationary Operational Environmental Satellite (GOES) Imager

4. 4 of 26 Background and Motivation Better understanding of aerosol characteristics is needed for Local/regional scale : Air quality and Health Global scale : Climate Complexity of aerosol system – 6 dimensions (x,y,z,t,size,composition) The number of unknown aerosol parameters is larger than that can be detected by any individual measurements existing today Recent developments in surface and satellite sensing along with new information technologies now allow real- time, 'just-in-time' data analysis for the characterization of atmospheric aerosols

5. 5 of 26 Objective of PhD Thesis Improving understanding on the atmospheric aerosol system by using combined earth observations Development of strategy for aerosol characterization through multi-sensor data integration

6. 6 of 26 Multi-sensor approach MISR MODIS Ground Monitoring Geostationary Sensor OMI AERONET LIDAR Facilities Visibility Measurements

7. 7 of 26 Benefits of Integration Using multi-sensor approach, information on the following aerosol properties can be made available for integration and interpretation : Spatial distribution Temporal behavior Optical properties Vertical distribution Emission source locations Transport

8. 8 of 26 Potential Earth Observations for Integration Geostationary sensors GOES/Meteosat/VHRR Polar orbiting sensors MODIS/MISR/SeaWiFs/TOMS/OMI Ground based observations PM/Speciation data Visibility data

9. 9 of 26 Data Fusion  Data fusion is a group of methods and approaches using multisource data of different nature to increase the quality of information contained in the data (Mangolini,1994).  A relatively new field in its application to environmental remote sensing  A rich history in physiology and neural sciences, as well as their artificial counterpart in robotics, where automated mechanisms combine information from multiple knowledge sources to improve the understanding of a given scene

10. 10 of 26 Levels of Data Fusion (Falke et al., 2001)

11. 11 of 26 Aspects of Data Fusion: Some questions to be asked (Pohl and van Genderen,1998) Which types of data are the most useful for meeting the objective? Which is the `best’ technique of fusing these data types for that particular application? What are the necessary pre-processing steps involved? Which combination of the data is the most successful?

12. 12 of 26 Example of Asian Dust Event,1998 The underlying color image is the surface reflectance derived from SeaWiFS. The TOMS absorbing aerosol index (level 2.0) is superimposed as green contours. The image for April 19 contains two additional data sets from the NRL surface observation data base: the red contours represent the surface wind speed and the blue circles indicate locations where dust was observed. ( Falke et al., 2001)

13. 13 of 26 Aerosol remote sensing from Geostationary platforms Advantages Good Temporal Resolution Full disc view Varying view geometry Limitations Coarse spatial resolution Few spectral bands

14. 14 of 26 Preliminary Work for Aerosol Retrieval from A Geostationary Sensor (GOES)

15. 15 of 26 About GOES Imager Channel No Wave Length Range (µm) Spatial Resolution 10.52-0.711km 23.73-4.074km 35.80-7.304km 410.20-11.204km 613.00-13.704km

16. 16 of 26 Work carried out with GOES Imager Pre-Processing of GOES-12 Imager Geometry file creation (Sun-Target-Sensor Angles) Georeferencing Clean surface generation (Surface reflectance) Compositing method Cloud masking Spatial & Spectral thresholds Twilight zone observations of aerosols

17. 17 of 26 Retrieval of surface reflectance Minimum Pixel value image generated from compositing images for 15 days GOES12-Band1-20070511-1730UTC

18. 18 of 26 Work carried out with GOES Imager Pre-Processing of GOES-12 Imager Geometry file creation Georeferencing Clean surface generation (Surface reflectance) Compositing method Cloud masking Spatial & Spectral thresholds Twilight zone observations of aerosols

19. 19 of 26 Cloud masking Filter1 : Band-6 (13.31 micrometer) deficit threshold filter Filter2 : Band-4 (10.71 micrometer) deficit threshold filter Filter3 : Band-4 to Band-6 ratio threshold filter Filter4 : Band-1 threshold filter GOES12-Band1-20070511-1730UTC

20. 20 of 26 Work carried out with GOES Imager Pre-Processing of GOES-12 Imager Geometry file creation Georeferencing Clean surface generation (Surface reflectance) Compositing method Cloud masking Spatial & Spectral thresholds Twilight zone observations of aerosols

21. 21 of 26 Twilight Zone (Shah, 1968)

22. 22 of 26 Sun-Target-Sensor Angle Variation Over a Day Dawn Dusk Noon GOES 12 73° West

23. 23 of 26 Twilight zone observations of aerosols May 14,2007 17 31 45 UTCMay 15,2007 02 31 44 UTC

24. 24 of 26 Work- Schedule for next year

25. 25 of 26 Acknowledgements Professor Virendra Sethi – PhD Advisor RPC Members : Professor R.S.Patil and Dr. Inamdar Indo-US Science &Technology Forum Professor Rudolf Husar CAPITA,WUStl AAQRL Colleagues CESE Staff

26. 26 of 26 Thank you