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Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing directions are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky images taken from the ground and backscatter profiles from a lidar.

A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

Optical probes are frequently used for the detection of microphysical cloud particle properties such as liquid and ice phase, size and morphology. These properties can eventually influence the angular light scattering properties of cirrus clouds as well as the growth and accretion mechanisms of single cloud particles. In this study we compare four commonly used optical probes to examine their response to small cloud particles of different phase and asphericity. Cloud simulation experiments were conducted at the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at European Organisation for Nuclear Research (CERN). The chamber was operated in a series of multi-step adiabatic expansions to produce growth and sublimation of ice particles at super- and subsaturated ice conditions and for initial temperatures of −30, −40 and −50 °C. The experiments were performed for ice cloud formation via homogeneous ice nucleation. We report the optical observations of small ice particles in deep convection and in situ cirrus simulations. Ice crystal asphericity deduced from measurements of spatially resolved single particle light scattering patterns by the Particle Phase Discriminator mark 2 (PPD-2K, Karlsruhe edition) were compared with Cloud and Aerosol Spectrometer with Polarisation (CASPOL) measurements and image roundness captured by the 3View Cloud Particle Imager (3V-CPI). Averaged path light scattering properties of the simulated ice clouds were measured using the Scattering Intensity Measurements for the Optical detectioN of icE (SIMONE) and single particle scattering properties were measured by the CASPOL. We show the ambiguity of several optical measurements in ice fraction determination of homogeneously frozen ice in the case where sublimating quasi-spherical ice particles are present. Moreover, most of the instruments have difficulties of producing reliable ice fraction if small aspherical ice particles are present, and all of the instruments cannot separate perfectly spherical ice particles from supercooled droplets. Correlation analysis of bulk averaged path depolarisation measurements and single particle measurements of these clouds showed higher R2 values at high concentrations and small diameters, but these results require further confirmation. We find that none of these instruments were able to determine unambiguously the phase of the small particles. These results have implications for the interpretation of atmospheric measurements and parametrisations for modelling, particularly for low particle number concentration clouds.

Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermodenuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error, and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.

The Optical Spectrograph and Infra-Red Imager System (OSIRIS) and the Atmospheric Chemistry Experiment (ACE) have been taking measurements from space since 2001 and 2003, respectively. This paper presents intercomparisons between ozone and NO2 measured by the ACE and OSIRIS satellite instruments and by ground-based instruments at the Polar Environment Atmospheric Research Laboratory (PEARL), which is located at Eureka, Canada (80° N, 86° W) and is operated by the Canadian Network for the Detection of Atmospheric Change (CANDAC). The ground-based instruments included in this study are four zenith-sky differential optical absorption spectroscopy (DOAS) instruments, one Bruker Fourier transform infrared spectrometer (FTIR) and four Brewer spectrophotometers. Ozone total columns measured by the DOAS instruments were retrieved using new Network for the Detection of Atmospheric Composition Change (NDACC) guidelines and agree to within 3.2%. The DOAS ozone columns agree with the Brewer spectrophotometers with mean relative differences that are smaller than 1.5%. This suggests that for these instruments the new NDACC data guidelines were successful in producing a homogenous and accurate ozone dataset at 80° N. Satellite 14–52 km ozone and 17–40 km NO2 partial columns within 500 km of PEARL were calculated for ACE-FTS Version 2.2 (v2.2) plus updates, ACE-FTS v3.0, ACE-MAESTRO (Measurements of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation) v1.2 and OSIRIS SaskMART v5.0x ozone and Optimal Estimation v3.0 NO2 data products. The new ACE-FTS v3.0 and the validated ACE-FTS v2.2 partial columns are nearly identical, with mean relative differences of 0.0 ± 0.2% and −0.2 ± 0.1% for v2.2 minus v3.0 ozone and NO2, respectively. Ozone columns were constructed from 14–52 km satellite and 0–14 km ozonesonde partial columns and compared with the ground-based total column measurements. The satellite-plus-sonde measurements agree with the ground-based ozone total columns with mean relative differences of 0.1–7.3%. For NO2, partial columns from 17 km upward were scaled to noon using a photochemical model. Mean relative differences between OSIRIS, ACE-FTS and ground-based NO2 measurements do not exceed 20%. ACE-MAESTRO measures more NO2 than the other instruments, with mean relative differences of 25–52%. Seasonal variation in the differences between NO2 partial columns is observed, suggesting that there are systematic errors in the measurements and/or the photochemical model corrections. For ozone spring-time measurements, additional coincidence criteria based on stratospheric temperature and the location of the polar vortex were found to improve agreement between some of the instruments. For ACE-FTS v2.2 minus Bruker FTIR, the 2007–2009 spring-time mean relative difference improved from −5.0 ± 0.4% to −3.1 ± 0.8% with the dynamical selection criteria. This was the largest improvement, likely because both instruments measure direct sunlight and therefore have well-characterized lines-of-sight compared with scattered sunlight measurements. For NO2, the addition of a ±1° latitude coincidence criterion improved spring-time intercomparison results, likely due to the sharp latitudinal gradient of NO2 during polar sunrise. The differences between satellite and ground-based measurements do not show any obvious trends over the missions, indicating that both the ACE and OSIRIS instruments continue to perform well.

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and the cross-dependence on humidity are the leading error sources. We introduce an a posteriori correction method of the cross-dependence on humidity, and we recommend applying it to isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

The Maïdo high-altitude observatory located in Reunion Island (21° S, 55.5° E) is equipped with Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset and lamp measurements. The mean relative standard error on the calibration coefficient is around 2.7 %. Two years of lidar water vapor measurements from November 2013 to October 2015 are now processed. By comparing CFH (Cryogenic Frost point Hygrometer) radiosonde profiles with the Raman lidar profiles, the ability of the lidar to provide accurate measurements is possible up to 22 km. The ability of measuring water vapor mixing ratios of a few ppmv in the lower stratosphere is demonstrated with a 48-hours integration time period, an absolute error lower than 0.8 ppmv and a relative error less than 20 %. This Raman lidar is dedicated to provide regular profiles of water vapor measurements with a high vertical resolution and low uncertainties to international networks; in the wider interest of research on stratosphere-troposphere exchange processes and on the long-term survey of water vapor in the upper troposphere and lower stratosphere in the Southern Hemisphere. A strategy of data sampling and filtering is proposed to meet these objectives with regard to the altitude range requested. 10-min time integration and 65–90 m vertical resolution ensure a vertical profile reaching 10 km, but more than 2800 minutes and a vertical resolution of 150–1300 m are necessary to reach the lower stratosphere with an uncertainty less than 20 %.

The possibility of measuring aerosol optical absorption properties in the UV spectral range such as single scattering albedo (SSA), using remote sensing techniques, is currently an open scientific issue. We investigate the limitations on calculating column average SSA using a combination of global UV spectral measurements (that are comon in various UV monitoring stations worldwide) with radiative transfer modeling. To point out the difficulties in such a retrieval we have used the travelling reference spectroradiometer QASUME (Quality Assurance of Spectral Ultraviolet Measurements in Europe) results from 27 visits to UV monitoring stations around Europe. We have used the QASUME instrument as relative reference, analyzing absolute differences and also temporal and spectral deviations of UV irraidances, that are used as basic input for the SSA retrieval. The results comparing the mean SSA derived by all instruments, measuring synchronous UV spectra, showed that 5 were within ± 0.02 difference from the SSA calculated from the QASUME instrument, while 17 were within ± 0.04, for the Solar zenith angle of 60 degrees. As for the uncertainty that has been calculated using the 2σ standard deviation of the spectral measurements, a mean 0.072 and 0.10 (2σ) uncertainties have been calculated for 60° and 30°, respectively. Based on the fact that additional uncertainties would be introduced in the SSA retrieval from AOD model input accuracy, assymetry parameter assumptions, we show that only very few instrumnents could be able to detect long term SSA changes. However, such measurements/results ar useful in order to retrieve SSA at UV wavelengths, a product needed for various applications such as, inputs for modeling radiative forcing studies and satellite retrieval algorithms.

Measurements of aerosol dynamics in the sub-10 nm size range are crucially important for quantifying the impact of new particle formation onto the global budget of cloud condensation nuclei. Here we present the development and characterization of a differential mobility analyzer train (DMA-train), operating six DMAs in parallel for high-time-resolution particle-size-distribution measurements below 10 nm. The DMAs are operated at six different but fixed voltages and hence sizes, together with six state-of-the-art condensation particle counters (CPCs). Two Airmodus A10 particle size magnifiers (PSM) are used for channels below 2.5 nm while sizes above 2.5 nm are detected by TSI 3776 butanol-based or TSI 3788 water-based CPCs. We report the transfer functions and characteristics of six identical Grimm S-DMAs as well as the calibration of a butanol-based TSI model 3776 CPC, a water-based TSI model 3788 CPC and an Airmodus A10 PSM. We find cutoff diameters similar to those reported in the literature. The performance of the DMA-train is tested with a rapidly changing aerosol of a tungsten oxide particle generator during warmup. Additionally we report a measurement of new particle formation taken during a nucleation event in the CLOUD chamber experiment at CERN. We find that the DMA-train is able to bridge the gap between currently well-established measurement techniques in the cluster–particle transition regime, providing high time resolution and accurate size information of neutral and charged particles even at atmospheric particle concentrations.

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.