Abstract
In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).
Highlights
The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR)
In addition to the method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem (Case 1 above), methods relying on Bayesian networks are described
To test the neural networks, we considered a specific problem, namely the retrieval of cloud optical thickness and cloud top height from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR)
Summary
In addition to the method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem (Case 1 above), methods relying on Bayesian networks are described These methods, in which the network activations and weights can be modeled by parametric probability distributions, are standard tools for uncertainty predictions in deep neural networks, and can be applied to nonlinear retrieval problems, and to the best knowledge of the authors, have not yet been used in atmospheric remote sensing.
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