Satellite detection, long‐range transport, and air quality impacts of volcanic sulfur dioxide from the 2014–2015 flood lava eruption at Bárðarbunga (Iceland)
Journal of Geophysical Research: Atmospheres American Geophysical Union (AGU) 120:18 (2015) 9739-9757
The vertical distribution of volcanic SO2 plumes measured by IASI
Copernicus Publications 15:17 (2015) 24643-24693
ERACE: The environmental response to aerosols observed in CCI ECVs
Sixth ESA CCI collocation meeting European Space Agency (2015)
Known and unknown unknowns: Uncertainty estimation in satellite remote sensing data
RSPSoc - NCEO - CEOI-ST Joint Conference Centre for Instrumentation (2015)
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.The application of optimal estimation to lidar
RSPSoc - NCEO - CEOI-ST Joint Conference Centre for Instrumentation (2015)