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Retrieval of Aerosol Properties from Single-view Satellite Imagery

Supervisor: Prof Don Grainger (Department of Physics, University of Oxford)
Advisor: Dr Adam Povey (Department of Physics, University of Oxford)

Scientific Background

Atmospheric aerosols and cloud influence the climate by scattering and absorbing light. The most recent IPCC report labelled these interactions the most uncertain aspect of our understanding of the Earth's radiation budget. An accurate and long-term record of aerosol properties is essential to understanding the mechanics of our atmosphere, monitoring the effects of wildfires, and adapting to the growing problem of air pollution.

As part of a European Space Agency project, researchers at Oxford helped produce a 30+ year record of cloud properties from satellite observations. A successful DPhil candidate will join that team and devise a means to extend our aerosol record to cover early measurements which are single view. The method could be adapted to the next generation of geostationary imagery, providing near-real-time observations of aerosol and cloud of unrivaled detail and duration.

Figure 1. The interaction of aerosol and clouds in a volcanic plume. Yellow-red contours shows the prevalence of sulpur dioxide emitted during the 2013 erurption of Calbuco. Green-blue boxes give the aerosol optical depth and the greyscale background displays the cloud optical depth. (Credit: Adam Povey and Cat Hayer.)


This project aims to produce an accurate retrieval of aerosol properties from single-view satellite imagery.


The Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm is open-source implementation of optimal estimation retrieval developed by EODG in conjunction with scientists at the Rutherford Appleton Laboratory. The code is used to infer aerosol (and cloud) properties from radiometer imagers, such as MODIS, the Sea and Land Surface Temperature Radiometer (SLSTR) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Currently, ORAC requires two views of the atmosphere to produce an aerosol retrieval. This limits us to observations since 1995 despite acceptable satellite records beginning in 1978. This also excludes observations from the SEVIRI geostationary imagers, which provide planetary-scale imagery every 15-minutes. Aerosol retrievals at such resolution are necessary to evaluate some of the most uncertain processes.

This project will determine the best means to produce aerosol retrievals from single-view imagery. This is expected to involve constraining the surface reflectance, through some combination of theoretical modelling and independent observations. Various single-view aerosol retrievals exist, such as NASA's Deep Blue, that are expected to provide a starting point.

Depending on the student's interest, there is scope to manage and operate remote sensing instruments in Oxford to generate data that can be compared to the new retrievals for validation. Products can also be intercompared to trace gas and volcanic products generated within the group.

Year 1 Review literature; Reproduce an existing methodology within the ORAC code; Quantify the information content of single-view retrievals; Implement a new single-view retrieval within ORAC.
Year 2 Generate aerosol retrievals from MODIS, AVHRR, and/or SEVIRI; Investigate a case study of aerosol-cloud interactions using that data; Write up the technique as an academic paper.
Year 3 At the student's discretion, investigate a further topic associated with the new dataset; Write-up thesis.

Skills that would be helpful:

Computing, statistics, atmospheric radiative transfer, remote sensing (especially of the surface).

Relevant Background Documents

Thomas, G.E., C.A. Poulsen, R. Siddans, A.M. Sayer, E. Carboni, S.H. Marsh, S.M. Dean R.G. Grainger and B.N. Lawrence, Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean, Atmospheric Chemistry and Physics, 10, 48494866, 2010.

Earth Observation Data Group, Department of Physics, University of Oxford. Page last updated: @12:46 GMT 20-Aug-2021