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Satellite product validation is a 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 as radiosonde. This is especially true for essential climate variables such as temperature and humidity.

Thanks to a functional data representation, this paper uses a likelihood-based approach that exploits the measurement uncertainties in a natural way. In particular, the comparison of temperature and humidity radiosonde measurements collected within the network of the Universal Rawinsonde Observation Program (RAOB) and the corresponding atmospheric profiles derived from the infrared atmospheric sounding interferometer aboard 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 the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN), which provides high-resolution fully traceable radio-sounding profiles. In this way, the uncertainty related to coarse vertical resolution, or sparseness, of the 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-vapor 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-vapor mixing ratio.