The ability of anthropogenic aerosols to freeze supercooled cloud droplets remains debated. In this work, we present observational evidence for the glaciation of supercooled liquid-water clouds at industrial aerosol hot spots at temperatures between −10° and −24°C. Compared with the nearby liquid-water clouds, shortwave reflectance was reduced by 14% and longwave radiance was increased by 4% in the glaciation-affected regions. There was an 8% reduction in cloud cover and an 18% reduction in cloud optical thickness. Additionally, daily glaciation-induced snowfall accumulations reached 15 millimeters. Glaciation events downwind of industrial aerosol hot spots indicate that anthropogenic aerosols likely serve as ice-nucleating particles. However, rare glaciation events downwind of nuclear power plants indicate that factors other than aerosol emissions may also play a role in the observed glaciation events.

Despite different techniques for the estimation of column integrated water vapour (precipitable water, PW) no method has yet been identified as the most accurate or the reference one. In this work we report intercomparisons between four PW estimation methods – radiosonde, Aerosol Robotic Network (AERONET), Global Positioning System (GPS), and High Resolu­tion Limited Area Model (HIRLAM). Two intensive observation periods at Tõravere, Estonia, were used: 22 June-6 November 2008 and 9-12 August 2010. During the longer campaign, only observations by GPS, AERONET, and HIRLAM were performed. An agreement with average difference less than 2.2% among all three methods was established. However, compared to HIRLAM and GPS, the AERONET method overestimated PW by 5–9% at PW  25 mm. In addition, the consistency test applied indicated that previously reported uncertainty in AERONET-measured PW is too high. During the shorter but more complex campaign, data obtained with all four methods were available. Although the average differences between PW from radiosonde and three other methods were

A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement.

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.