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Abstract:

This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data - known, quantified "unknowns". The under-constrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed - known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity - unknown "unknowns". The latter two categories of error are dominant in under-constrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.

Abstract:

An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and auxiliary data – known, quantified `unknowns'. The underconstrained nature of most satellite remote sensing observations requires the use of approximations and assumptions that produce non-linear systematic errors that are not readily assessed – known, unquantifiable `unknowns'. Additional errors result from the inability of a measurement to resolve all scales and aspects of variation in a system – unknown `unknowns'. The latter two categories of error are dominant in satellite remote sensing and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. Ensemble techniques present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties, generated using various algorithms or forward models believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more accurate representation of the uncertainty as understood by the data producer and greater freedom to exploit the advantages and disadvantages of different manners of describing a physical system. The technique will be demonstrated with retrievals of aerosol, cloud, and surface properties, for which many sources of error cannot currently be quantified (such as the assumed aerosol microphysical properties). The Optimal Retrieval of Aerosol and Cloud (ORAC) can produce an ensemble by evaluating data with a succession of microphysical models (e.g. liquid cloud, urban aerosol, etc.). A further ensemble can be formed from products produced by various European institutions. These will be used to demonstrate uncertainties in such observations that are poorly characterised in current products.