Paolo Antonelli

TL;DR: In this paper, a simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra.

TL;DR: In this paper, principal component analysis (PCA) was applied to reduce the random noise present in the hyperspectral infrared observations, and the results obtained depend on the variability of selected sets of observations and on specific instrument characteristics such as spectral resolution and noise statistics.

TL;DR: In this article, an improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements using CO2 sorting, which determines optimal channel pairs to which the CO2 slicing retrieval will be applied.

TL;DR: Retrieval exercises performed in simulation and with real observations lead us to conclude that the principal components space-based inverse approach is potentially superior over the current practice of using sparse channels.

TL;DR: The European AQUA Thermodynamic Experiment (EAQUATE) was held in Italy and the United Kingdom to demonstrate certain ground-based and airborne systems useful for validating hyperspectral satellite sounding observations as mentioned in this paper.