The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (${\mathrm{XCH}}_4$) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of ${\mathrm{CH}}_4$ fluxes using the CarbonTracker Europe-${\mathrm{CH}}_4$ (CTE-${\mathrm{CH}}_4$) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where ${\mathrm{CH}}_4$ emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher ${\mathrm{CH}}_4$ emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH₄) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH₄ than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH₄ difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH₄ difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH₄ (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH₄ differences comparable to or smaller than those from ground-based remote sensing.

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.

Spectrometers are designed to isolate particular wavebands and suppress light from wavelengths outside the band of interest. However, a small amount of undesired light will always enter the detector, not through the designed optical path, but through random scattering from the instrument optical components, housing, and dust particles. Every spectrophotometer has stray light coming from outside the nominal measurement waveband. For Dobson spectrophotometers and single monochromator Brewer spectrophotometers, which are basic instruments in the World Meteorological Organization (WMO) ozone and ultraviolet (UV) monitoring network, the error introduced by stray light is substantial when the ozone slant path becomes very large because of high solar zenith angles and a thick ozone layer. These are common conditions during Arctic spring. To study the issue, a long ozone slant path Intercomparison/Calibration campaign for Nordic Brewers and Dobsons was held at Sodankylä 8–24 March 2011 and a follow-up campaign to extend calibrations to shorter ozone slant paths took place at Izaña observatory, Tenerife, between 28 October and 18 November 2011. These campaigns were part of the Committee on Earth Observation Satellites (CEOS) Intercalibration of Ground-based Spectrometers and Lidars project funded by the European Space Agency (ESA), intended to permit the homogenization of ozone data from the European ozone ground-truthing network. During the active intercomparison periods, measurements were taken only when good conditions for sun or moon observations existed. Laboratory measurements using calibration lamps and helium-cadmium (HeCd) lasers were an essential part of both campaigns. The campaigns produced a high-quality database of total ozone and UV measurements and an accurate, up-to-date calibration and characterization of participating Brewers and Dobsons against the European standard instruments from the Regional Dobson Calibration Centre-Europe (RDCC-E) and the Regional Brewer Calibration Centre-Europe (RBCC-E). In the present work we focus on single monochromator Brewers and present a physics-based method to compensate for the stray-light effects in ozone retrieval using laboratory characterizations and radiative transfer modelling. The method was tested with independent data from the campaign.

Aerosols play an important role in key atmospheric processes and feature high spatial and temporal variabilities. This has motivated scientific interest in the development of networks capable of measuring aerosol properties over large geographical areas in near-real time. In this work we present and discuss results of an aerosol optical depth (AOD) algorithm applied to instruments of the European Brewer Network. This network is comprised of close to 50 Brewer spectrophotometers, mostly located in Europe and adjacent areas, although instruments operating at, for example, South America and Australia are also members. Although we only show results for instruments calibrated by the Regional Brewer Calibration Center for Europe, the implementation of the AOD algorithm described is intended to be used by the whole network in the future. Using data from the Brewer intercomparison campaigns in the years 2013 and 2015, and the period in between, plus comparisons with Cimel sun photometers and UVPFR instruments, we check the precision, stability, and uncertainty of the Brewer AOD in the ultraviolet range from 300 to 320 nm. Our results show a precision better than 0.01, an uncertainty of less than 0.05, and, for well-maintained instruments, a stability similar to that of the ozone measurements. We also discuss future improvements to our algorithm with respect to the input data, their processing, and the characterization of the Brewer instruments for the measurement of AOD.