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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 12
Atmos. Chem. Phys., 15, 7039–7048, 2015
https://doi.org/10.5194/acp-15-7039-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 7039–7048, 2015
https://doi.org/10.5194/acp-15-7039-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Jun 2015

Research article | 30 Jun 2015

Balancing aggregation and smoothing errors in inverse models

A. J. Turner1 and D. J. Jacob1,2 A. J. Turner and D. J. Jacob
  • 1School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
  • 2Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, USA

Abstract. Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

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