The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being $MAE\cong 0.32$ K, and smaller at a high latitude, $MAE\cong 0.21$ K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with $MAE<0.2$ K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K.

The intercomparison among different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde, radio occultation (e.g. GNSS-RO) and atmospheric model outputs (e.g. ERA5 reanalysis). Since instruments and/or measures are not perfectly collocated, the miss-collocation uncertainty must be considered in the related intercomparison uncertainty budgets.

Accurate and reliable observational data are essential for understanding climate dynamics, assessing climate change impacts, and supporting informed adaptation strategies. This work highlights the pivotal role of reference measurements and climate data records accompanied by robust uncertainty quantification, in ensuring the quality and consistency of observational datasets. These datasets underpin scientific analyses and decision-making processes, particularly in the context of climate studies and applications. Within the framework of the Copernicus program, the European Centre for Medium-Range Weather Forecasts (ECMWF) operates the Copernicus Climate Change Service (C3S) with funding from the European Union. The Institute of Methodologies for Environmental Analysis (IMAA) of the National Research Council (CNR) has been engaged by ECMWF to implement the C3S2 311 Lot2 project, which builds on previous efforts in Copernicus contracts to enhance access to high-quality baseline and reference observations. This project underscores the importance of standardized measurement protocols and rigorous uncertainty assessment methodologies. The study introduces three types of datasets made available on the Copernicus Climate Data Store (CDS), which serves as a key platform for disseminating reference datasets, ensuring accessibility for researchers and stakeholders (1) upper-air reference measurements for GRUAN, (2) near-surface reference measurements from USCRN, and (3) precipitable water vapor derived from reference and reprocessed GNSS time series. The potential applications of these measurements in characterizing atmospheric conditions and investigating climate variability are discussed.

The Global Climate Observing System (GCOS), supported by a number of international partners and the World Meteorological Organization (WMO), is establishing a reference climate observation network, the GCOS Reference Upper Air Network (GRUAN). Currently, this network comprises 30 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity in the upper atmosphere, thus providing a cornerstone to more reliable monitoring of signals of global and regional climate change. GRUAN observations are required to be of reference quality, with known biases removed and with an associated uncertainty value, based on thorough characterization of all sources of measurement.In support of these goals, GNSS precipitable water (GNSS-PW) measurement has been included as a priority one measurement of the essential climate variable water vapor. In addition, a minimum of twice daily measurements (ideally hourly measurement) of PW are required as entrance to the GRUAN program. GNSS-PW is the primary means to accomplish this entrance requirement. The GNSS-PW program produces a nearly continuous reference measurement of PW and is therefore a substantial part of GRUAN. GFZ contributes to GRUAN with its expertise in processing of ground-based GNSS network data to generate precise PW products. Since 2013, GFZ hosts a GRUAN central processing facility for the GNSS-PW. GFZ is responsible for the installation of GNSS hardware, data transfer, processing and archiving, derivation of GNSS PW products according to GRUAN requirements including PW uncertainty estimation, as well as for quality check and archiving of the GNSS-PW products. Currently half of the GRUAN sites are equipped with GNSS receivers. At the beginning of this year the data processing of GNSS-PW and its associated documentation has been GRUAN certified. GNSS-PW products including uncertainty estimation and results of selected validation studies are presented.