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AERONET versus MODIS retrievals at different spatial resolutions over south-east Italy M. Santese, F. De Tomasi and M. R. Perrone Physics Department, University of Lecce, Italy (email@example.com / Fax: +39 0832 – 297505) Geographic location of the AERONET monitoring site
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AERONET versus MODIS retrievals at different spatial resolutions over south-east Italy
M. Santese, F. De Tomasi and M. R. Perrone
Physics Department, University of Lecce, Italy (firstname.lastname@example.org / Fax: +39 0832 – 297505)
Geographic location of the AERONET monitoring site
Aerosol optical thicknesses AOTs and Fine Fraction parameters η retrieved by AERONET measurements from March 2003 to September 2004 at Lecce’s University, are compared to similar MODIS_Terra data retrieved over ocean and land-ocean at 550 nm and at different spatial resolutions (50x50, 100x100, and 300x300km²) co-located in space and time.
AERONET versus MODIS temporal evolution
AERONET and MODIS-ocean
fine fraction parameters
AERONET versus MODIS AOTs
The correlation factors R of linear regressions spanthe 0.88-0.83range.
MODIS AOTs meet expected uncertainties:
Over ocean70%, 67%, and 70% of data points of the 50x50 km², 100x100 km², and 300x300 km² window size, respectively is within expected uncertainties;
Over land-ocean 85%, 88%, and 82% of AOT values retrieved at 50x50 km², 100x100 km², and 300x300 km² window size, respectivelymeets pre-specified accuracy conditions.
MODIS overestimates AOTs at low aerosol loadings. This result can be due to the fact that the two algorithms understimate the ground surface reflectance.
The slopes of the over land-ocean regression lines is closer to unity: the land-ocean MODIS AOT values can better represent the aerosol properties over south-east Italy.
MODIS AOTs follow the temporal evolution of AERONET AOTs at all tested window sizes and are characterized by a significant seasonal dependence.
The temporal evolutions of ocean and land-ocean mean AOTs are not dependent on window size.
1. Contribute to the validation of MODIS aerosol products over
south-east Italy and investigate the correlation dependence
on spatial resolution and identify regional biases of Lecce’s
2.Can MODIS help us to understand to what extent the Lecce’s
AERONET site can be considered representative of a larger area
and hence, locally-derived aerosol parameters can be of use in
General Circulation and Chemical Transport Models?
The ηMtemporal evolution is not affected by the window size: monthly average values of the 50x50 km² window size are rather similar to those of 300x300 km² window size.
ηM monthly means depend on seasons and take values in the 0.7- 0.8 and 0.4- 0.6 range in spring-summer and autumn-winter, respectively.
ηA monthly means span the 0.7- 0.8 range during all year and are not significantly affected by seasons.
It is possible that the marked seasonal evolution of ηMis mostly due to the MODIS-ocean algorithm that underestimates the fine fraction contribution on autumn-winter months.
Fig.3(a),(b) Temporal plots (red dots) of MODIS-ocean fine fraction ηM(c)temporal plot(reddots)ofthe AERONET fine fraction parameterηA. Black full dots and error bars represent monthly average values and corresponding standard deviations. Blue boxes show on each panel the monthly distribution of data points.
Fig. 2: Scatter plots of MODIS AOT referring to the 50x50, 100x100, and 300x300 km² window size centered on Lecce versus AERONET AOT mean values collocated in time. Solid red and black lines represent the linear regression lines and the 1:1 lines, respectively, dashed lines are MODIS pre launch expected uncertanties.
Fig.2 : Temporal evolution of MODIS (blue dots) and collocated in time AERONET (red dots) AOTs referring to the 50x50, 100x100, and 300x300 km². Open blue and red dots represent monthly averaged values of MODIS and AERONET AOTs collocated in time, respectively.
° MODIS AOT meet expected uncertainties;
° Regression lines fitting ocean- and land-ocean-MODIS AOT values indicate that MODIS overestimates AOTs at low aerosol loadings;
° The slope of the regression lines fitting the scatterplots with land-ocean-MODIS AOTs is closer to unity: the land-ocean-MODIS AOTs better represent the aerosol properties over south-east Italy.
The temporal evolution of the MODIS fine fraction ηM(fig. 3) depends on seasons, while the AERONET fine fraction ηAdoesn’t vary during all year: MODIS-ocean algorithm underestimates the fine fraction contribution on autumn-winter?
Despite previous investigations on the validation of MODIS retrievals, the results of this study refer to a single site on south-east Italy where different aerosol types may converge duringthe year and manyaerosol types can superimpose mainly in summer as a consequence of the weather stability.
Then, the area can be well suited to test the performance of MODIS retrieval algorithms.
°All these results can allow inferring that AERONET AOTs retrieved at Lecce can be considered representative at least of a 300x300 km² area centered on Lecce.
° Hence locally-derived aerosol parameters can be of use in General Circulation and Chemical Transport Models.