Abstract
Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.
Highlights
Aerosols, which are suspended particulate matter in the atmosphere, are widely considered to be one of the most uncertain components of the global climate with important influence on cloud properties and radiative forcing
We first assess the variability of results for these different simulations, so that we can understand the importance of our choice of aerosol models and their impact on simulated uncertainty
We present an analysis of the information content in multi-angle, multi-spectral and polarimetric observations for the retrieval of fine size mode aerosol and surface optical properties
Summary
Aerosols, which are suspended particulate matter in the atmosphere, are widely considered to be one of the most uncertain components of the global climate with important influence on cloud properties and radiative forcing. Most instruments do not provide information about all the parameters needed to reduce uncertainties in the radiative forcing due to aerosols [5]. These parameters include aerosol total atmospheric extinction, size distribution, particle shape, complex refractive index and vertical distribution, generally for multiple size modes. Accurate and independent values of only some parameters can be extracted from the data, while others must be assumed based upon external information or historical observations. The impact of these assumptions on retrieval uncertainty is often difficult to characterize [9, 10]
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