Spatial Retrievals of Carbon Dioxide from the OCO-2 Satellite

• 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
• 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

Abstract

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.