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Abstract:
We adapt general statistical methods to estimate
the optimal error covariance matrices in a regional inversion
system inferring methane surface emissions from atmospheric
concentrations. Using a minimal set of physical hypotheses
on the patterns of errors, we compute a guess of
the error statistics that is optimal in regard to objective statistical
criteria for the specific inversion system. With this
very general approach applied to a real-data case, we recover
sources of errors in the observations and in the prior state of
the system that are consistent with expert knowledge while
inferred from objective criteria and with affordable computation
costs. By not assuming any specific error patterns, our
results depict the variability and the inter-dependency of errors
induced by complex factors such as the misrepresentation
of the observations in the transport model or the inability
of the model to reproduce well the situations of steep
gradients of concentrations. Situations with probable significant
biases (e.g., during the night when vertical mixing is
ill-represented by the transport model) can also be diagnosed
by our methods in order to point at necessary improvement
in a model. By additionally analysing the sensitivity of the
inversion to each observation, guidelines to enhance data selection
in regional inversions are also proposed. We applied
our method to a recent significant accidental methane release
from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.
