  Satellite product validation is key to ensure the delivery of quality
products for climate and weather applications. To do this, a fundamental step
is the comparison with other instruments, such us radiosonde. This is specially
true for Essential Climate Variables such as temperature and humidity. Thanks
to a functional data representation, this paper uses a likelihood based
approach which exploits the measurement uncertainties in a natural way. In
particular the comparison of temperature and humdity radiosonde measurements
collected within RAOB network and the corresponding atmospheric profiles
derived from IASI interferometers aboard of Metop-A and Metop-B satellites is
developed with the aim of understanding the vertical smoothing mismatch
uncertainty. Moreover, conventional RAOB functional data representation is
assessed by means of a comparison with radiosonde reference measurements given
by GRUAN network, which provides high resolution fully traceable radiosouding
profiles. In this way the uncertainty related to coarse vertical resolution, or
sparseness, of conventional RAOB is assessed. It has been found that the
uncertainty of vertical smoothing mismatch averaged along the profile is 0.50 K
for temperature and 0.16 g/kg for water vapour mixing ratio. Moreover the
uncertainty related to RAOB sparseness, averaged along the profile is 0.29 K
for temperature and 0.13 g/kg for water vapour mixing ratio.

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