Professor Roy Grainger

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.

Abstract:

Lidars are ideally placed to investigate the effects of aerosol and cloud on the climate system due to their unprecedented vertical and temporal resolution. Dozens of techniques have been developed in recent decades to retrieve the extinction and backscatter of atmospheric particulates in a variety of conditions. These methods, though often very successful, are fairly ad hoc in their construction, utilising a wide variety of approximations and assumptions that makes comparing the resulting data products with independent measurements difficult and their implementation in climate modelling virtually impossible. As with its application to satellite retrievals at the turn of the century, the methods of non-linear regression can improve this situation by providing a mathematical framework in which the various approximations, estimates of experimental error, and any additional knowledge of the atmosphere can be clearly defined and included in a mathematically `optimal' retrieval method, providing rigorously derived error estimates. In addition to making it easier for scientists outside of the lidar field to understand and utilise lidar data, it also simplifies the process of moving beyond extinction and backscatter coefficients and retrieving microphysical properties of aerosols and cloud particles. A technique to estimate the lidar's overlap function using an analytic model of the optical system and a simple extinction profile has been developed. This is used to calibrate the system such that the profile of extinction and backscatter coefficients can be retrieved using the elastic and nitrogen Raman backscatter signals. These methods have been used to extract value from compromised data collected with a prototype Raman lidar system. Selected events will be presented, with the hope that others may be inspired to apply the techniques to a more robust system.