Abstract
Abstract. In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements – combining neural networks and an iterative scheme based on Phillips–Tikhonov regularization – is described. The algorithm – which is an extension of a scheme previously designed for ground-based retrievals – is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips–Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.
Highlights
Multi-angular, multispectral measurements of intensity and linear polarization parameters of scattered solar radiation are a useful tool for the characterization of atmospheric aerosols (Mishchenko and Travis, 1997; Hasekamp and Landgraf, 2007)
In an experiment performed on ground-based spectropolarimetric measurements, it has been shown (Di Noia et al, 2015) that the final result of an iterative aerosol retrieval may depend on the choice of the first guess, and that replacing a LUT-based first guess with a neural network algorithm may be beneficial for the algorithm convergence and computation time, as it is relatively simple to design neural networks that provide quicker and more accurate first guess retrievals than reasonably sized LUTs with modest computational effort
In this paper we have demonstrated the application of neural networks to aerosol retrievals from the Research Scanning Polarimeter (RSP) instrument
Summary
Multi-angular, multispectral measurements of intensity and linear polarization parameters of scattered solar radiation are a useful tool for the characterization of atmospheric aerosols (Mishchenko and Travis, 1997; Hasekamp and Landgraf, 2007). In an experiment performed on ground-based spectropolarimetric measurements, it has been shown (Di Noia et al, 2015) that the final result of an iterative aerosol retrieval may depend on the choice of the first guess, and that replacing a LUT-based first guess with a neural network algorithm may be beneficial for the algorithm convergence and computation time, as it is relatively simple to design neural networks that provide quicker and more accurate first guess retrievals than reasonably sized LUTs with modest computational effort Extending this approach to aircraft and satellite measurements is possible, in principle, once a method is devised for taking the variability of the observation geometry into account when a training set for the neural network is generated.
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