  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 well on this task, but both schemes are computationally heavy
on large quantities of data. Kernel methods do not scale well with the number
of training data, and neural networks require setting critical hyperparameters.
In this work we follow an alternative pathway: we study transfer learning in
convolutional neural nets (CNN s) to alleviate the retraining cost by departing
from proxy solutions (either features or networks) obtained from previously
trained models for related variables. We show how features extracted from the
IASI data by a CNN trained to predict a physical variable can be used as inputs
to another statistical method designed to predict a different physical variable
at low altitude. In addition, the learned parameters can be transferred to
another CNN model and obtain results equivalent to those obtained when using a
CNN trained from scratch requiring only fine tuning.
