0000000001317129
AUTHOR
David Malmgren-hansen
Spatial noise-aware temperature retrieval from infrared sounder data
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…
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
IASI dataset v1
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series measures the infrared spectrum with high resolution. The ground footprint resolution of the instruments is 12 km at nadir, and a spectral resolution of 0.25cm −1 in the spectrum between 645 cm −1 and 2760 cm −1 . This results in 8461 spectral samples covering 2200km scan-swath with 60 points per line. IASI is an ideal instrument for monitoring different physical/chemical parameters in the atmosphere e.g. temperature, humidity and trace gases such as ozone. Energy from different altitudes returns a different spectral shift. In this way atmospheric profiles can be obtained and these provides important …