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Activity Number: 450 - Quantitative Inference for the Global Carbon Cycle
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #300029
Title: Spatial Retrievals of Carbon Dioxide from the OCO-2 Satellite
Author(s): Matthias Katzfuss* and Jonathan Hobbs and Jenny Brynjarsdottir and Anirban Mondal and Daniel Zilber
Companies: Texas A & M University and Jet Propulsion Laboratory and Case Western Reserve University and Case Western Reserve University and
Keywords: spatial statistics; atmospheric science; satellite data; remote sensing; carbon cycle; inverse problem

Atmospheric quantities such as CO2 concentrations are retrieved by inferring their values from indirect measurements consisting of reflectance intensities in different spectral bands. To date, these retrievals are usually carried out separately for each pixel or spatial location. We propose to carry out spatial retrievals, which solve the retrieval problem simultaneously for multiple neighboring pixels. Spatial retrievals can exploit the fact that atmospheric variables vary smoothly over space, especially at higher altitudes where they are well mixed. Hence, atmospheric properties at nearby pixels are strongly correlated. By translating this correlation structure into a regularization term, spatial smoothness is enforced probabilistically. This essentially allows spatial retrievals to borrow information from measurements at nearby pixels, thereby resulting in more accurate retrievals.

Authors who are presenting talks have a * after their name.

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