100528 satellite obs_china_husar
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Seminar at Fudan University, May 28, 2010, Shanghai, China
Rudolf B. HusarWith
Li Du, Erin RobinsonWashington University, St. Louis, MO, USA
Spatial and Temporal Pattern of Air Pollution over China Based on
Remote Sensing Observations
Atmospheric Aerosol Challenge:Characterization of Aerosols
• Aerosol complexity is due 7-dim. data space• The ‘aerosol dimensions’ D, C, S determine the
effects on health and climate
Dimension Abbr. Data SourcesSpatial 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 concentration: a (X, Y, Z, T, D, C, S)
Challenge: Vertical Distribution of Aerosols
Vertical Distribution:
• Layering• Size Distr.• Composition
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
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 HazeLyons W.A., Husar R.B. Mon. Weather Rev. 1976
SMS GOES June 30 1975
Satellites show the synoptic aerosol patternand provide rich spatial context …
e.g. pollution in valleys.
Jan 10, 2003, SeaWiFS Dec 19, 2007, MODIS
The Perfect Dust Storm…Apr. 7, 2001 SaeWiFS
Asian Dust Cloud over N. America
On April 27, the dust cloud arrived in North America.
Regional average PM10 concentrations increased to 65 g/m3
Asian Dust
100 g/m3
Hourly PM10
In Washington State, PM10 concentrations exceeded 100 g/m3
Satellite(MODIS, OMI)
Visual Range(WMO)
Sun Photometer(Aeronet)
Challenge: Aerosol Retrieval Quality
MODIS vs. MISR: Poor AOT Correlation over Land
Land Ocean
MIS
R A
OT
MODIS-AOT MODIS-AOT
Aerosol AOT – MODISJanuary April
OctoberJuly
AERONET – MODIS AOT Comparison
AeronetMODIS
MODIS/Aeronet Ratio
Beijing
Hong Kong
Correlations good but systematic differences (slope)
MODIS4 AOT: Thursday
MODIS4 AOT: Sunday
MODIS4 AOT: Thursday
MODIS4 AOT: Sunday
MODIS Fire Pixels
OMI Absorbing Aerosol Index
January April
OctoberJuly
OMI CHCO Formaldehyde
January April
OctoberJuly
Population density
Emissions - NOx
Satellite Column Concentration: NO2, 2005
OMI Spectrometer Sensor
Satellite Column Concentration: NO2, 2009
OMI Spectrometer Sensor
OMI NO2 Day of Week: Thursday
OMI NO2 Day of Week: Sunday
Visibility is recorded at 7000+ stations hourly
Visual Range, Guilin 2010-05-23,24
Visual Range 16 km
Visual Range 9 km
Guilin
Surface Extinction Coefficient
Jun, Jul, Aug Dec, Jan, Feb
Sechuan Basin
Chengdu
Chengdu
Jun, Jul, Aug Chongqing
ChongqingDec, Jan, Feb
MODIS VISIBILITY
Xi’an
Xi’an
Xi’an
Jun, Jul, Aug Tianjin
TianjinDec, Jan, Feb
MODIS VISIBILITY
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.
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