Abstract
Abstract. A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis.
Highlights
The development of satellite high-spectral resolution infrared spectrometers is expected to improve quality and density of retrieval of atmospheric parameters
Supposing that R is the identity operator, than Functional Sliced Inverse Regression (FSIR) aims at determining the directions along which to project the data by the eigenvalues-eigenvectors decomposition of e, which takes into account the information about the profiles by means of a regression on the spectra; on the contrary the Principal Component Analysis (PCA) uses only spectral information
A new statistical strategy based on dimension reduction for the retrieval of atmospheric parameters from Infrared Atmospheric Sounding Interferometer (IASI) radiances has been presented and discussed
Summary
The development of satellite high-spectral resolution infrared spectrometers is expected to improve quality and density of retrieval of atmospheric parameters. Amato et al.: Dimension-reduction for IASI radiances methodology is based on a suitable statistical dimension reduction technique, FSIR Supposing that R is the identity operator, than FSIR aims at determining the directions along which to project the data by the eigenvalues-eigenvectors decomposition of e, which takes into account the information about the profiles by means of a regression on the spectra; on the contrary the PCA uses only spectral information. Y (M))t , is made up with the layer-mean estimated values of a given parameter, in order to form the profile function of the parameter, the retrieval covariance matrix can be obtained by. Where expectation value has to be taken with respect to training data set and Y true is the true value of the parameter This matrix will be denoted by T , H2O and O3 for temperature, water vapour and ozone profiles, respectively. To limit the burden of the computational effort, only nadir view soundings have been considered
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call