Abstract
Atmospheric observations from space often result in spectral data of large dimensions. To allow an optimal inversion of the observed spectra it can be necessary to map the data into a space of smaller dimension. Here several data reduction techniques based on eigenvector expansions of the spectral space are compared. The comparison is done by inverting simulated observations from a microwave limb sounder, the Odin-SMR. For the examples tested, reductions exceeding two orders of magnitude with no negative influence on the retrieval performance are demonstrated. The techniques compared include a novel method developed especially for atmospheric inversions, based on the weighting functions of the variables to be retrieved. The new method shows an excellent performance in practical tests and is both computationally more effective and more flexible than the standard Hotelling transformation.
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