Spatial noise-aware temperature retrieval from infrared sounder data
This work offers an incremental improvement in atmospheric temperature retrieval accuracy for meteorologists and climate scientists, potentially leading to more precise weather forecasting and climate monitoring.
This paper addresses the retrieval of atmospheric temperature profiles from infrared sounder data by incorporating spatial information and a noise-dependent dimensionality reduction technique. The study demonstrates that using Minimum Noise Fraction (MNF) for dimensionality reduction significantly improves error rates compared to the widely used Principal Component Analysis (PCA).
In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using MNF instead. In our analysis we also investigate the relationship between error rate improvements when including more spectral and spatial components in the regression model, aiming to uncover the trade-off between model complexity and error rates.