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| Titel |
A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion |
| VerfasserIn |
J. Ray, J. Lee, V. Yadav, S. Lefantzi, A. M. Michalak, B. Bloemen Waanders |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 4 ; Nr. 8, no. 4 (2015-04-29), S.1259-1273 |
| Datensatznummer |
250116289
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| Publikation (Nr.) |
 copernicus.org/gmd-8-1259-2015.pdf |
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| Zusammenfassung |
Atmospheric inversions are frequently used to estimate fluxes of atmospheric
greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface.
These inversions typically assume that flux departures from a prior model are
spatially smoothly varying, which are then modeled using a multi-variate
Gaussian. When the field being estimated is spatially rough, multi-variate
Gaussian models are difficult to construct and a wavelet-based field model
may be more suitable. Unfortunately, such models are very high dimensional
and are most conveniently used when the estimation method can simultaneously
perform data-driven model simplification (removal of model parameters that
cannot be reliably estimated) and fitting. Such sparse reconstruction methods
are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the
estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states
of the USA.
Our new method is based on stagewise orthogonal matching pursuit (StOMP), a
method used to reconstruct compressively sensed images. Our adaptations
bestow three properties to the sparse reconstruction procedure which are
useful in atmospheric inversions. We have modified StOMP to incorporate prior
information on the emission field being estimated and to enforce
non-negativity on the estimated field. Finally, though based on wavelets, our
method allows for the estimation of fields in non-rectangular geometries, e.g.,
emission fields inside geographical and political boundaries.
Our idealized inversions use a recently developed multi-resolution (i.e.,
wavelet-based) random field model developed for ffCO2 emissions and
synthetic observations of ffCO2 concentrations from a limited set of
measurement sites. We find that our method for limiting the estimated field
within an irregularly shaped region is about a factor of 10 faster than
conventional approaches. It also reduces the overall computational cost by
a factor of 2. Further, the sparse reconstruction scheme imposes
non-negativity without introducing strong nonlinearities, such as those
introduced by employing log-transformed fields, and thus reaps the benefits
of simplicity and computational speed that are characteristic of linear
inverse problems. |
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