Statistical harmonization and uncertainty assessment in the comparison of satellite and radiosonde climate variables
Francesco Finazzi, Alessandro Fass\`o, Fabio Madonna, Ilia Negri,, Bomin Sun, Marco Rosoldi

TL;DR
This study develops a likelihood-based method to compare satellite and radiosonde climate variables, quantifying uncertainties due to measurement and resolution differences to improve climate data validation.
Contribution
It introduces a functional data approach for harmonizing satellite and radiosonde measurements, explicitly quantifying vertical smoothing and sparseness uncertainties.
Findings
Vertical smoothing mismatch uncertainty: 0.50 K for temperature, 0.16 g/kg for humidity.
RAOB sparseness uncertainty: 0.29 K for temperature, 0.13 g/kg for humidity.
Method effectively quantifies measurement uncertainties in climate variable comparisons.
Abstract
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…
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