Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of ground-based instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2). This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.

We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure < ∼ 50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ∼ 550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6 %, and the standard deviations (SDs) range from 5 to 10 % for pressure < ∼ 50 hPa to less than 18 % (27 %) in the tropics (midlatitudes) for pressure > ∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of < 5 % and the MBs of the tropospheric ozone column (TOC) are within 6 % with SDs of 15 %. In the high latitudes, the profile MBs are within 10 % with SDs of 5–15 % for pressure < ∼ 50 hPa but increase to 30 % with SDs as great as 40 % for pressure > ∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure > ∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14–0.7 % year⁻¹ for pressure < ∼ 80 hPa, 0.7 DU year⁻¹ in SOC, and −0. 33 DU year⁻¹ in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

We present a cloud-screening method based on differential optical absorption spectroscopy (DOAS) measurements, more specifically using intensity measurements and O₄ differential slant-column densities (DSCDs). Using the colour index (CI), i.e. the ratio of the radiance at two wavelengths, we define different sky conditions including clear, thin clouds/polluted, fully-cloudy, and heavily polluted. We also flag the presence of broken and scattered clouds. The O₄ absorption is a good tracer for cloud-induced light-path changes and is used to detect clouds and discriminate between instances of high aerosol optical depth (AOD) and high cloud optical depth (COD). We apply our cloud screening to MAX-DOAS (multi-axis DOAS) retrievals at three different sites with different typical meteorological conditions, more specifically suburban Beijing (39.75° N, 116.96° E), Brussels (50.78° N, 4.35° E) and Jungfraujoch (46.55° N, 7.98° E). We find that our cloud screening performs well characterizing the different sky conditions. The flags based on the colour index are able to detect changes in visibility due to aerosols and/or (scattered) clouds. The O₄-based multiple-scattering flag is able to detect optically thick clouds, and is needed to correctly identify clouds for sites with extreme aerosol pollution. Removing data taken under cloudy conditions results in a better agreement, in both correlation and slope, between the MAX-DOAS AOD retrievals and measurements from other co-located instruments.

The ozonesonde stations at Uccle (Belgium) and De Bilt (the Netherlands) are separated by only 175 km but use different ozonesonde types (or different manufacturers for the same electrochemical concentration cell (ECC) type), operating procedures, and correction strategies. As such, these stations form a unique test bed for the Ozonesonde Data Quality Assessment (O3S-DQA) activity, which aims at providing a revised, homogeneous, consistent dataset with an altitude-dependent estimated uncertainty for each revised profile. For the ECC ozonesondes at Uccle mean relative uncertainties in the 4–6 % range are obtained. To study the impact of the corrections on the ozone profiles and trends, we compared the Uccle and De Bilt average ozone profiles and vertical ozone trends, calculated from the operational corrections at both stations and the O3S-DQA corrected profiles. In the common ECC 1997–2014 period, the O3S-DQA corrections effectively reduce the differences between the Uccle and De Bilt ozone partial pressure values with respect to the operational corrections only for the stratospheric layers below the ozone maximum. The upper-stratospheric ozone measurements at both sites are substantially different, regardless of the correction methodology used. The origin of this difference is not clear. The discrepancies in the tropospheric ozone concentrations between both sites can be ascribed to the problematic background measurement and correction at De Bilt, especially in the period before November 1998. The Uccle operational correction method, applicable to both ozonesonde types used, diminishes the relative stratospheric ozone differences of the Brewer–Mast sondes in the 1993–1996 period with De Bilt to less than 5 % and to less than 6 % in the free troposphere for the De Bilt operational corrections. Despite their large impact on the average ozone profiles, the different (sensible) correction strategies do not change the ozone trends significantly, usually only within their statistical uncertainty due to atmospheric noise. The O3S-DQA corrections bring the Uccle and De Bilt ozone trend estimates for 1997–2014 closer to each other in the lower stratosphere and lower troposphere. Throughout the whole vertical profile, these trend estimates are, however, not significantly different from each other, and only in the troposphere significantly positive. For the entire Uccle observation period (1969–2014), the operational corrections lead to height-independent and consistent ozone trends for both the troposphere and the stratosphere, with rates of +2 to +3 % decade⁻¹ and −1 to −2 % decade⁻¹, respectively.

Radiation fog occurs over many locations around the world in stable atmospheric conditions. Air traffic at busy airports can be significantly disrupted because low visibility at the ground makes it unsafe to take off, land and taxi on the ground. Current numerical weather prediction forecasts are able to predict general conditions favorable for fog formation, but not the exact time or location of fog occurrence. A selected set of observations available in near-real time at strategic locations could also be useful to track the evolution of key processes and key parameters that drive fog formation. Such observations could complement the information predicted by numerical weather prediction (NWP) models that is made available to airport forecasters in support of their fog forecast. This paper presents an experimental setup based on collocated automatic lidar and ceilometer measurements, relative humidity measurements and horizontal visibility measurements to study hygroscopic growth of fog condensation nuclei. This process can take several minutes to hours, and can be tracked using lidar- or ceilometer-attenuated backscatter profiles. Based on hygroscopic growth laws we derive a set of parameters that can be used to provide alerts minutes to hours prior to formation of radiation fog. We present an algorithm that uses the temporal evolution of attenuated backscatter measurements to derive pre-fog formation alerts. The performance of the algorithm is tested on 45 independent pre-fog situations at two locations (near Paris, France, and Brussels, Belgium). We find that an alert for pre-fog conditions predominantly occurs 10–50 min prior to fog formation at an altitude ranging 0 to 100 m above ground. In a few cases, alerts can occur up to 100 min prior to fog formation. Alert durations are found to be sensitive to the relative humidity conditions found a few hours prior to the fog.

Review of the existing bibliography shows that the direction and magnitude of the long-term trends of UV irradiance, and their main drivers, vary significantly throughout Europe. Analysis of total ozone and spectral UV data recorded at four European stations during 1996–2017 reveals that long-term changes in UV are mainly driven by changes in aerosols, cloudiness, and surface albedo, while changes in total ozone play a less significant role. The variability of UV irradiance is large throughout Italy due to the complex topography and large latitudinal extension of the country. Analysis of the spectral UV records of the urban site of Rome, and the alpine site of Aosta reveals that differences between the two sites follow the annual cycle of the differences in cloudiness and surface albedo. Comparisons between the noon UV index measured at the ground at the same stations and the corresponding estimates from the Deutscher Wetterdienst (DWD) forecast model and the ozone monitoring instrument (OMI)/Aura observations reveal differences of up to 6 units between individual measurements, which are likely due to the different spatial resolution of the different datasets, and average differences of 0.5–1 unit, possibly related to the use of climatological surface albedo and aerosol optical properties in the retrieval algorithms.