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Publication - Dr Matthew Watson

    Evaluating nonlinear optimal estimation uncertainty in cloud and aerosol remote sensing


    Western, LM, Rougier, J & Watson, IM, 2019, ‘Evaluating nonlinear optimal estimation uncertainty in cloud and aerosol remote sensing’. Atmospheric Science Letters.


    Uncertainty estimates are important when retrieving properties of clouds and aerosols from satellites measurements. These measurements must be interpreted using a form of inverse theory, such as optimal estimation. In atmospheric remote sensing these inverse methods often assume that the forward model is linear in the region of uncertainty. This assumption is not necessarily valid. This paper presents an exact confidence procedure in contrast to the linear approximation using a maximum likelihood estimator. A simple ex- ample of retrieving the effective radius and 10.8 μm optical depth of a volcanic ash cloud shows that a linear approximation to uncertainty does not describe nonlinear optimal estimation uncertainty well for the given example. The presented exact confidence procedure is especially useful for inference where the entire parameter space has been for- ward modelled prior to or during the retrieval, such as using look up tables, where it is less computationally expensive than a linear approximation.

    Full details in the University publications repository