Gerrit de Leeuw 1,2,3 , Larisa Sogacheva1, Pekka Kolmonen 1 , Anu- Maija Sundström 2 , Edith Rodriguez 1 1 FMI, Climate Change Unit, Helsinki, Finland 2 Univ. of Helsinki, Dept. of Physics, Helsinki, Finland 3 TNO, Utrecht, Netherlands Retrieval of aerosol properties using AATSR
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Gerrit de Leeuw1,2,3, Larisa Sogacheva1, Pekka Kolmonen1, Anu-Maija Sundström2,
Edith Rodriguez1
1 FMI, Climate Change Unit, Helsinki, Finland
2 Univ. of Helsinki, Dept. of Physics, Helsinki, Finland
Schematic representation of DV- and SV- algorithms
Satellite observation:•Instrument characteristics•Calibration•Cloud and surface effects
Radiative Transfer Model : DAK (Double Adding KNMI)•Optical properties aerosol•Meteorology
Actual retrieval
SV:Over ocean
Crucial steps in aerosol retrieval• Cloud screening: any residual cloud in a scene
results in high AOD
• Surface contributions:
• Eliminate: multiple view
• Dark surface over ocean, with Ocean surface reflectance model
• Radiative transfer model
• Compare modeled reflectance at top of atmosphere with measurement
• ’Best fit’ provides desired aerosol parameters
Test for August 10th 2004Comparison with MODIS
Cloud Protocol : 4 tests•BT12µm
• R659
• R865nm/R659
• BT12µm – BT11µm Not always used Agreement: 83.03 % Disagreement: 12.05 % Non conclusive: 4.91 %
AATSR CLOUD MASK
CLEAR
CLOUD
AATSR CLOUD MASK
CLEAR
Cloud Screening
Aerosol models in ADV:• based on AERONET
observations • Dubovik et al. 2002: Variability of
absorption and optical properties of key aerosol types observed in worldwide locations. J ATMOS SCI, 59 (3): 590-608.
• Robles-Gonzalez et al. 2002, Aerosol properties over the Indian Ocean Experiment (INDOEX) campaign area retrieved from ATSR-2, J. Geophys. Res., 111, D15205, doi:10.1029/2005JD006184.
• Levy et al. 2007: Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. J. Geophys.
Res., 112, D13210.
ADV Aerosol Remote Sensing Applications
Europe: 2003, 2006, 2008
2003, yearly avarage
(Pekka Kolmonen)
ADV Aerosol Remote Sensing Applications
Europe: forest fires Iberian Peninsula, 11 August 2003
(Anu-Maija Sundström)
ADV Aerosol Remote Sensing Applications
Europe: focus on Po Valley (TEMIS)
2003, yearly average
(Pekka Kolmonen)
2003, July-August average
0.9
ADV Aerosol Remote Sensing Applications
Europe: clean air over Finland
02 May 2006
(Larisa Sogacheva)
04 June 2008
ADV Aerosol Remote Sensing Applications
Focus on China
0.55 m; 25 July 2008Variability (0.55 m):
March 2008
(Anu-Maija Sundström)
3.0
ADV Aerosol Remote Sensing ApplicationsFocus on China
(Anu-Maija Sundström)
ADV Aerosol Remote Sensing ApplicationsFocus on China
(Anu-Maija Sundström)
Single overpass: 19Oct2008 Zoom over Beijing area
3
Aerosol Remote Sensing Applications
Po Valley
Regional: Europe
Smoke Iberian Peninsula
Finland:clean air N
ote scales
0.7
0.2
1.8
0.7
China, Beijing
CONCLUSION: ADV works for very low AOD over Finland (~0.05) to very high AOD over China (~3)
Data sets• Available:
• Europe 2003, 2006, 2008
• Zoom on Po-Valley
• China 8 months in 2008, more to come
• Amazone: work in progress
• Africa, India, Brazil, Beijing: EUCAARI Development Countries: work in progress
• Ocean: AMARSI algo tested (AATSR / MERIS) and ready for use
• Global: MACC: 2 years, work in progress
• All AATSR data received on LTO tape (7/2002 – 4/2009)
Conclusions• The AATSR Dual view algorithms works over land in a variety of
conditions• No a priori info on surface needed, but could improve the results• Aerosol models through LUT approach; could be improved • Some reasonable results have been obtained over the desert
over the UAE (bright surface), but little or no dust• Cloud screening reasonable, but may be further improved • Dust detection often fails, a dust detection algorithm developed
for SEVIRI over ocean (Bennouna et al., 2009, in press) is tested for AATSR
SEVIRI: Dust Retrieval over Ocean
Bennouna et al., 2009, JGR Atmospheres, in press
Conclusions• AATSR data archive (7/2002-4/2009) received and read• NRT under development:
• Uses rolling archive • Long time series: ATSR-2 – AATSR – SLSTA
(1995 – present .. 2013 >)• Problems:
• AATSR swath limits coverage• Clouds• Snow
Use of AATSR data in GLOBemissions• Data assimilation: data interval too long (3 days at
mid latitudes or more when clouds)
• Hotspot detection
• Localized sources, such as:
• Forest fire emissions
• Power plants
• (needed for inventories)
• Inversion?
• Advantage with respect to ’operational’ products: