Kernel-based retrieval of atmospheric profiles from IASI data
This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification of anomalies by supervised classification of discrepancies with the ECMWF estimates. For the retrieval, we observed that KRR clearly outperformed linear regression. Looking at the confidence maps, we observed that big discrepancies are mainly due to the presence of clouds and low emissivities in desert areas. For the identification of anomalies, we observed that the confidence intervals provided by the KRR may help in discarding big errors. High detection accuracy (around 90%) is achieved by a support vector machine, which largely outperforms standard linear and nonlinear classifiers.