Tracking the Aerosol Optical Depth (AOD) is of particular importance in monitoring aerosol contributions to global radiative forcing. Until now, the two standard techniques used for retrieving AOD were; (i) sun photometry, and (ii) satellite-based approaches, such as based DDV (Dense Dark Vegetation) inversion algorithms. These methods are only available for use during daylight time since they are based on direct or indirect observation of sunlight. Few attempts have been made to measure AOD behaviour at night. One such method uses spectrally-calibrated stars as reference targets but the number of available stars is limited. This is especially true for urban sites where artificial lighting hides most of these stars. In this research, we attempt to provide an alternate method, one which exploits artificial sky glow or light pollution. This methodology links a 3D light pollution model with in situ light pollution measurements. The basic idea is to adjust an AOD value into the model in order to fit measured light pollution. This method requires an accurate model that includes spatial heterogeneity in lighting angular geometry, in lighting spectral dependence, in ground spectral reflectance and in topography. This model, named ILLUMINA, computes 1st and 2nd order molecular and aerosol scattering, as well as aerosol absorption. These model features represent major improvements to previous light pollution models. Therefore, new possibilities for light pollution studies will arise, many of which are of particular interest to the astronomical community. In this paper we will present a first sensitive study applied to the ILLUMINA model.
Nicolas Pfister, Norman O'Neill, Martin Aube, Minh-Nghia Nguyen, Xavier Bechamp-Laganiere, Albert Besnier, Louis Corriveau, Geremie Gasse, Etienne Levert, Danick Plante
Satellite-based measurements of aerosol optical depth (AOD) over land are obtained from an inversion procedure applied to dense dark vegetation pixels of remotely sensed images. The limited number of pixels over which the inversion procedure can be applied leaves many areas with little or no AOD data. Moreover, satellite coverage by sensors such as MODIS yields only daily images of a given region with four sequential overpasses required to straddle mid-latitude North America. Ground based AOD data from AERONET sun photometers are available on a more continuous basis but only at approximately fifty locations throughout North America. The object of this work is to produce a complete and coherent mapping of AOD over North America with a spatial resolution of 0.1 degree and a frequency of three hours by interpolating MODIS satellite-based data together with available AERONET ground based measurements. Before being interpolated, the MODIS AOD data extracted from different passes are synchronized to the mapping time using analyzed wind fields from the Global Multiscale Model (Meteorological Service of Canada). This approach amounts to a trajectory type of simplified atmospheric dynamics correction method. The spatial interpolation is performed using a weighted least squares method applied to bicubic B-spline functions defined on a rectangular grid. The least squares method enables one to weight the data accordingly to the measurement errors while the B-splines properties of local support and C2 continuity offer a good approximation of AOD behaviour viewed as a function of time and space.
Measurements of aerosol optical depth (AOD) are important indicators of aerosol particle behavior. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as DDV (Dense Dark Vegetation) based inversion algorithms which yield AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. We attempt to fill gaps in spatio-temporal AOD measurements using a new assimilation methodology that links AOD measurements and the predictions of a particulate matter Transport Model. This modelling package (AODSEM V2.0 for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution may be tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important and robust parameter. We applied this methodology to a significant smoke event that occurred over the eastern part of North America in July 2002.
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