Satellite-based estimates of greenhouse gas (GHG) properties from
observations of reflected solar spectra are integral for understanding and
monitoring complex terrestrial systems and their impact on the carbon cycle due
to their near global coverage. Known as retrieval, making GHG concentration
estimations from these observations is a non-linear Bayesian inverse problem,
which is operationally solved using a computationally expensive algorithm
called Optimal Estimation (OE), providing a Gaussian approximation to a
non-Gaussian posterior. This leads to issues in solver algorithm convergence,
and to unrealistically confident uncertainty estimates for the retrieved
quantities. Upcoming satellite missions will provide orders of magnitude more
data than the current constellation of GHG observers. Development of fast and
accurate retrieval algorithms with robust uncertainty quantification is
critical. Doing so stands to provide substantial climate impact of moving
towards the goal of near continuous real-time global monitoring of carbon
sources and sinks which is essential for policy making. To achieve this goal,
we propose a diffusion-based approach to flexibly retrieve a Gaussian or
non-Gaussian posterior, for NASA’s Orbiting Carbon Observatory-2 spectrometer,
while providing a substantial computational speed-up over the current
operational state-of-the-art.

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