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100528 satellite obs_china_husar
- 1. Spatial and TemporalPattern 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. Atmospheric Aerosol Challenge: Characterizationof 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. Challenge: Vertical Distributionof Aerosols Vertical Distribution: • Layering • Size Distr. • Composition
- 4. • Technical Challenge: Characterization PM characterizationrequires 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. Satellite Remote SensingSince 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. Satellites show thesynoptic aerosol pattern and provide rich spatial context … e.g. pollution in valleys. Jan 10, 2003, SeaWiFS Dec 19, 2007, MODIS
- 7. The Perfect DustStorm… Apr. 7, 2001 SaeWiFS
- 8. Asian Dust Cloudover 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
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- 10. MISR AOT Challenge: AerosolRetrieval Quality Land MODIS-AOT Ocean MODIS-AOT MODIS vs. MISR: Poor AOT Correlation over Land
- 11. Aerosol AOT –MODIS January April July October
- 12. AERONET – MODISAOT Comparison Beijing MODIS Aeronet MODIS/Aeronet Ratio Hong Kong Correlations good but systematic differences (slope)
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- 18. OMI Absorbing AerosolIndex January April July October
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- 21. Satellite Column Concentration:NO2, 2005 OMI Spectrometer Sensor
- 22. Satellite Column Concentration:NO2, 2009 OMI Spectrometer Sensor
- 23. OMI NO2 Dayof Week: Thursday
- 24. OMI NO2 Dayof Week: Sunday
- 25. Visibility is recordedat 7000+ stations hourly
- 26. Visual Range, Guilin2010-05-23,24
- 27. Visual Range 16km Visual Range 9 km
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- 29. Surface Extinction Coefficient Jun,Jul, Aug Dec, Jan, Feb
- 31. Sechuan Basin Jun, Jul,Aug Chengdu Chongqing Dec, Jan, Feb Chengdu Chongqing MODIS VISIBILITY
- 32. Xi’an Jun, Jul, Aug Xi’an Tianjin Dec,Jan, Feb Xi’an Tianjin MODIS VISIBILITY
- 33. Summary •Each data sethas 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.
- 34. International Collaboration Opportunity: GlobalObserving 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
Editor's Notes
- #6 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