Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 409974
Title Maximum likelihood estimation of covariance parameters for Bayesian atmospheric trace gas surface flux inversions
Author(s) Michalak, A.M.; Hirsch, A.; Bruhwiler, L.; Gurney, K.R.; Peters, W.; Tans, P.P.
Source Journal of Geophysical Research: Atmospheres 110 (2005)D24. - ISSN 2169-897X - 16 p.
Department(s) Meteorology and Air Quality
Publication type Refereed Article in a scientific journal
Publication year 2005
Keyword(s) carbon-dioxide - co2 inversions - transport - sinks - cycle - uncertainty - delta-c-13
Abstract This paper introduces a Maximum Likelihood (ML) approach for estimating the statistical parameters required for the covariance matrices used in the solution of Bayesian inverse problems aimed at estimating surface fluxes of atmospheric trace gases. The method offers an objective methodology for populating the covariance matrices required in Bayesian inversions, thereby resulting in better estimates of the uncertainty associated with derived fluxes and minimizing the risk of inversions being biased by unrealistic covariance parameters. In addition, a method is presented for estimating the uncertainty associated with these covariance parameters. The ML method is demonstrated using a typical inversion setup with 22 flux regions and 75 observation stations from the National Oceanic and Atmospheric Administration-Climate Monitoring and Diagnostics Laboratory (NOAA-CMDL) Cooperative Air Sampling Network with available monthly averaged carbon dioxide data. Flux regions and observation locations are binned according to various characteristics, and the variances of the model-data mismatch and of the errors associated with the a priori flux distribution are estimated from the available data
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