100528 satellite obs_china_husar

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  • The ATS was followed by the Synchronous Meteorological Satellite (SMS), the first series of geosynchronous weather satellites. SMS-1 was launched from Cape Canaveral, FL on May 17, 1974. It was the first operational satellite capable of detecting meteorological conditions from a fixed location
  • 100528 satellite obs_china_husar

    1. 1. Spatial and Temporal Pattern of Air Pollution over China Based on Remote Sensing Observations Rudolf B. Husar With Li Du, Erin Robinson Washington University, St. Louis, MO, USA Seminar at Fudan University, May 28, 2010, Shanghai, China
    2. 2. Atmospheric Aerosol Challenge: Characterization of Aerosols Dimension Abbr. Data Sources Spatial dimensions X, Y Satellites, dense networks Height Z Lidar, soundings Time T Continuous monitoring Particle size D Size-segregated sampling Particle Composition C Speciated analysis Particle Shape/Mixing S Microscopy, Source Type • • Aerosol complexity is due 7-dim. data space The ‘aerosol dimensions’ D, C, S determine the effects on health and climate Aerosol concentration: a (X, Y, Z, T, D, C, S)
    3. 3. Challenge: Vertical Distribution of Aerosols Vertical Distribution: • Layering • Size Distr. • Composition
    4. 4. • Technical Challenge: Characterization PM characterization requires many different instruments and analysis tools. • Each sensor/network covers only a fraction of the 7-Dim PM data space. Satellite-Integral Satellites, integrate over height, size, composition, shape… dimensions These data need de-convolution of the integral measures
    5. 5. Satellite Remote Sensing Since 1972 • • • First satellite aerosol paper, Francis Parmenter, 1972 Qualitative surface-satellite aerosol relationship shown, 1976 Focus on regional ‘hazy blobs’, sulfate pollution Regional Haze Lyons W.A., Husar R.B. Mon. Weather Rev. 1976 SMS GOES June 30 1975
    6. 6. Satellites show the synoptic aerosol pattern and provide rich spatial context … e.g. pollution in valleys. Jan 10, 2003, SeaWiFS Dec 19, 2007, MODIS
    7. 7. The Perfect Dust Storm… Apr. 7, 2001 SaeWiFS
    8. 8. Asian Dust Cloud over N. America In Washington State, PM10 100 µg/m3 concentrations exceeded 100 µg/m3 Asian Dust Hourly PM10 On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65 µg/m3
    9. 9. Satellite (MODIS, OMI) Sun Photometer (Aeronet) Visual Range (WMO)
    10. 10. MISR AOT Challenge: Aerosol Retrieval Quality Land MODIS-AOT Ocean MODIS-AOT MODIS vs. MISR: Poor AOT Correlation over Land
    11. 11. Aerosol AOT – MODIS January April July October
    12. 12. AERONET – MODIS AOT Comparison Beijing MODIS Aeronet MODIS/Aeronet Ratio Hong Kong Correlations good but systematic differences (slope)
    13. 13. MODIS4 AOT: Thursday
    14. 14. MODIS4 AOT: Sunday
    15. 15. MODIS4 AOT: Thursday
    16. 16. MODIS4 AOT: Sunday
    17. 17. MODIS Fire Pixels
    18. 18. OMI Absorbing Aerosol Index January April July October
    19. 19. OMI CHCO Formaldehyde January April July October
    20. 20. Population density Emissions - NOx
    21. 21. Satellite Column Concentration: NO2, 2005 OMI Spectrometer Sensor
    22. 22. Satellite Column Concentration: NO2, 2009 OMI Spectrometer Sensor
    23. 23. OMI NO2 Day of Week: Thursday
    24. 24. OMI NO2 Day of Week: Sunday
    25. 25. Visibility is recorded at 7000+ stations hourly
    26. 26. Visual Range, Guilin 2010-05-23,24
    27. 27. Visual Range 16 km Visual Range 9 km
    28. 28. Guilin
    29. 29. Surface Extinction Coefficient Jun, Jul, Aug Dec, Jan, Feb
    30. 30. Sechuan Basin Jun, Jul, Aug Chengdu Chongqing Dec, Jan, Feb Chengdu Chongqing MODIS VISIBILITY
    31. 31. Xi’an Jun, Jul, Aug Xi’an Tianjin Dec, Jan, Feb Xi’an Tianjin MODIS VISIBILITY
    32. 32. Summary •Each data set has limitations, but gives self-consistent global-scale observations • More detailed measurements are essential for the understanding •There are still enormous challenges in integrating multi-sensory data for characterizing aerosols. Combining global-scale remote sensing observations with detailed local observations and research conducted in China could yield faster progress.
    33. 33. International Collaboration Opportunity: Global Observing System of Systems (GEOSS) Pooling of Earth Observations nine Societal Benefit Areas Any Dataset Serves Many Communities Any Problem Requires Many Datasets New International Program - China is a Co-Chair of GEOSS EE

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