Atmospheric Measurement Techniques

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ISSN / EISSN : 1867-1381 / 1867-8548
Published by: Copernicus GmbH (10.5194)
Total articles ≅ 3,874
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, Uroš Jagodič, Luka Pirker, Miha Škarabot, Mario Kurtjak, Kristijan Vidović, , , Jannis Röhrbein, , et al.
Atmospheric Measurement Techniques, Volume 15, pp 3805-3825;

There exists a lack of aerosol absorption measurement techniques with low uncertainties and without artefacts. We have developed the two-wavelength Photothermal Aerosol Absorption Monitor (PTAAM-2λ), which measures the aerosol absorption coefficient at 532 and 1064 nm. Here we describe its design, calibration and mode of operation and evaluate its applicability, limits and uncertainties. The 532 nm channel was calibrated with ∼ 1 µmol mol−1 NO2, whereas the 1064 nm channel was calibrated using measured size distribution spectra of nigrosin particles and a Mie calculation. Since the aerosolized nigrosin used for calibration was dry, we determined the imaginary part of the refractive index of nigrosin from the absorbance measurements on solid thin film samples. The obtained refractive index differed considerably from the one determined using aqueous nigrosin solution. PTAAM-2λ has no scattering artefact and features very low uncertainties: 4 % and 6 % for the absorption coefficient at 532 and 1064 nm, respectively, and 9 % for the absorption Ångström exponent. The artefact-free nature of the measurement method allowed us to investigate the artefacts of filter photometers. Both the Aethalometer AE33 and CLAP suffer from cross-sensitivity to scattering – this scattering artefact is most pronounced for particles smaller than 70 nm. We observed a strong dependence of the filter multiple scattering parameter on the particle size in the 100–500 nm range. The results from the winter ambient campaign in Ljubljana showed similar multiple scattering parameter values for ambient aerosols and laboratory experiments. The spectral dependence of this parameter resulted in AE33 reporting the absorption Ångström exponent for different soot samples with values biased 0.23–0.35 higher than the PTAAM-2λ measurement. Photothermal interferometry is a promising method for reference aerosol absorption measurements.
Atmospheric Measurement Techniques, Volume 15, pp 3779-3803;

The chemical composition of ambient organic aerosols plays a critical role in driving their climate and health-relevant properties and holds important clues to the sources and formation mechanisms of secondary aerosol material. In most ambient atmospheric environments, this composition remains incompletely characterized, with the number of identifiable species consistently outnumbered by those that have no mass spectral matches in the literature or the National Institute of Standards and Technology/National Institutes of Health/Environmental Protection Agency (NIST/NIH/EPA) mass spectral databases, making them nearly impossible to definitively identify. This creates significant challenges in utilizing the full analytical capabilities of techniques which separate and generate spectra for complex environmental samples. In this work, we develop the use of machine learning techniques to quantify and characterize novel, or unidentifiable, organic material. This work introduces Ch3MS-RF (Chemical Characterization by Chromatography–Mass Spectrometry Random Forest Modeling), an open-source, R-based software tool, for efficient machine-learning-enabled characterization of compounds separated in chromatography–mass spectrometry applications but not identifiable by comparison to mass spectral databases. A random forest model is trained and tested on a known 130 component representative external standard to predict the response factors of novel environmental organics based on position in volatility–polarity space and mass spectrum, enabling the reproducible, efficient, and optimized quantification of novel environmental species. Quantification accuracy on a reserved 20 % test set randomly split from the external standard compound list indicates that random forest modeling significantly outperforms the commonly used methods in both precision and accuracy, with a median response factor percent error of −2 %, for modeled response factors, compared to > 15 %, for typically used proxy assignment-based methods. Chemical properties modeling, evaluated on the same reserved 20 % test set and an extrapolation set of species identified in ambient organic aerosol samples collected in the Amazon rainforest, also demonstrate robust performance. Extrapolation set property prediction mean absolute errors for carbon number, oxygen to carbon ratio (O : C), average carbon oxidation state (OSc ), and vapor pressure are 1.8, 0.15, 0.25, and 1.0 (log(atm)), respectively. Extrapolation set out-of-sample R2 for all properties modeled are above 0.75, with the exception of vapor pressure. While predictive performance for vapor pressure is less robust compared to the other chemical properties modeled, random-forest-based modeling was significantly more accurate than other commonly used methods of vapor pressure prediction, decreasing the mean vapor pressure prediction error to 0.24 (log(atm)) from 0.55 (log(atm)) (chromatography-based vapor pressure prediction) and 1.2 (log(atm)) (chemical formula-based vapor pressure prediction). The random forest model significantly advances an untargeted analysis of the full scope of chemical speciation yielded by two-dimensional gas chromatography (GCxGC-MS) techniques and can be applied to gas chromatography coupled with electron ionization mass spectrometry (GC-MS) as well. It enables the accurate estimation of key chemical properties commonly utilized in the atmospheric chemistry community, which may be used to more efficiently identify important tracers for further individual analysis and to characterize compound populations uniquely formed under specific ambient conditions.
, Xiquan Dong, Xiaojian Zheng, Peng Wu
Atmospheric Measurement Techniques, Volume 15, pp 3761-3777;

To investigate the cloud phase and macrophysical properties over the Southern Ocean (SO), the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF2) was installed on the Australian icebreaker research vessel (R/V) Aurora Australis during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign (41 to 69 S, 60 to 160 E) from October 2017 to March 2018. To examine cloud properties over the midlatitude and polar regions, the study domain is separated into the northern (NSO) and southern (SSO) parts of the SO, with a demarcation line of 60 S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the polar region. Low-level clouds and deep convective clouds are the two most common cloud types over the SO. A new method was developed to classify liquid, mixed-phase, and ice clouds in single-layered, low-level clouds (LOW), where mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freqs of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased by ∼8 K, and atmospheric precipitable water vapor increased from ∼5 mm at 69 S to ∼18 mm at 43 S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO, with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for the ice cloud Freq between NSO and SSO. The ice particle sizes are comparable to cloud droplets and drizzle drops and well mixed in the cloud layer. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.
Chuan Ping Lee, Mihnea Surdu, David M. Bell, , Mao Xiao, Xueqin Zhou, Andrea Baccarini, Stamatios Giannoukos, Günther Wehrle, Pascal André Schneider, et al.
Atmospheric Measurement Techniques, Volume 15, pp 3747-3760;

To elucidate the sources and chemical reaction pathways of organic vapors and particulate matter in the ambient atmosphere, real-time detection of both the gas and particle phase is needed. State-of-the-art techniques often suffer from thermal decomposition, ionization-induced fragmentation, high cut-off size of aerosols or low time resolution. In response to all these limitations, we developed a new technique that uses extractive electrospray ionization (EESI) for online gas and particle chemical speciation, namely the dual-phase extractive electrospray ionization time-of-flight mass spectrometer (Dual-Phase-EESI-TOF or Dual-EESI for short). The Dual-EESI was designed and optimized to measure gas- and particle-phase species with saturation vapor concentrations spanning more than 10 orders of magnitude with good linearity and a measurement cycle as fast as 3 min. The gas-phase selectivity of the Dual-EESI was compared with that of nitrate chemical ionization mass spectrometry. In addition, we performed organic aerosol uptake experiments to characterize the relative gas and particle response factors. In general, the Dual-EESI is more sensitive toward gas-phase analytes as compared to their particle-phase counterparts. The real-time measurement capability of the Dual-EESI for chemically speciated gas- and particle-phase measurements can provide new insights into aerosol sources or formation mechanisms, where gas-particle partitioning behavior can be determined after absolute parameterization of the gas / particle sensitivity.
, Mahdi Yousefi, Christopher Chan Miller, , , , Xiong Liu, Ewan O'Sullivan, Christopher E. Sioris, Steven C. Wofsy
Atmospheric Measurement Techniques, Volume 15, pp 3721-3745;

An optimal estimation-based algorithm is developed to retrieve the number density of excited oxygen (O2) molecules that generate airglow emissions near 0.76 µm (b1Σg+ or A band) and 1.27 µm (a1Δg or 1Δ band) in the upper atmosphere. Both oxygen bands are important for the remote sensing of greenhouse gases. The algorithm is applied to the limb spectra observed by the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument in both the nominal (tangent heights below ∼ 90 km) and mesosphere–lower thermosphere (MLT) modes (tangent heights spanning 50–150 km). The number densities of emitting O2 in the a1Δg band are retrieved in an altitude range of 25–100 km near-daily in 2010, providing a climatology of O2a1Δg-band airglow emission. This climatology will help disentangle the airglow from backscattered light in nadir remote sensing of the a1Δg band. The global monthly distributions of the vertical column density of emitting O2 in a1Δg state show mainly latitudinal dependence without other discernible geographical patterns. Temperature profiles are retrieved simultaneously from the spectral shapes of the a1Δg-band airglow emission in the nominal limb mode (valid altitude range of 40–100 km) and from both a1Δg- and b1Σg+ -band airglow emissions in the MLT mode (valid range of 60–105 km). The temperature retrievals from both airglow bands are consistent internally and in agreement with independent observations from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), with the absolute mean bias near or below 5 K and root mean squared error (RMSE) near or below 10 K. The retrieved emitting O2 number density and temperature provide a unique dataset for the remote sensing of greenhouse gases and constraining the chemical and physical processes in the upper atmosphere.
Atmospheric Measurement Techniques, Volume 15, pp 3683-3704;

We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high-quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. For 15 min means of DSSI, results from our preliminary implementation of Heliosat-V and ground-based measurements show a bias of 20 W m−2, a root-mean-square difference of 93 W m−2, and a correlation coefficient of 0.948. The statistics, except for the bias, are similar to operational and corrected satellite-based data products HelioClim3 version 5 and the CAMS Radiation Service.
, , Sophia Brilke, Julian Resch, Paul Martin Winkler, ,
Atmospheric Measurement Techniques, Volume 15, pp 3705-3720;

We present an electrical mobility classifier for mass–mobility measurements of atmospheric ions. Size segregation coupled with mass spectrometric detection of naturally occurring ions in the atmosphere is challenging due to the low ion concentration. Conventional electrical mobility classifying devices were not yet coupled with mass spectrometry to resolve natural ion composition. This is due to either the insufficient transmission efficiency or design concepts being incompatible with this application, e.g. using high electric fields close to the inlets to push ions from high to low electric potential. Here, we introduce an axial ion mobility classifier, termed AMC, with the aim to achieve higher transmission efficiencies to segregate natural ions at reasonable sizing resolution. Similar to the recently introduced principle of the high-pass electrical mobility filter (HP-EMF) presented by Bezantakos et al. (2015) and Surawski et al. (2017), ions are classified via an electric field that is opposed to the gas flow direction carrying the ions. Compared to the HP-EMF concept, we make use of sheath flows to improve the size resolution in the sub-3 nm range. With our new design we achieve a sizing resolution of 7 Z/ΔZ with a transmission efficiency of about 70 %.
, , Simon P. Alexander, , , Adrian McDonald
Atmospheric Measurement Techniques, Volume 15, pp 3663-3681;

Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.
, , Felix Ament
Atmospheric Measurement Techniques, Volume 15, pp 3641-3661;

This study elaborates on how aircraft-based horizontal geometries of trade wind cumulus clouds differ whether a one-dimensional (1D) profiler or a two-dimensional (2D) imager is used. While nadir profiling devices are limited to a 1D realization of the cloud transect size, with limited representativeness of horizontal cloud extension, 2D imagers enhance our perspectives by mapping the horizontal cloud field. Both require high resolutions to detect the lower end of the cloud size spectrum. In this regard, the payload aboard the HALO (High Altitude and LOng Range Research Aircraft) achieves a comparison and also a synergy of both measurement systems. Using the NARVAL II (Next-Generation Aircraft Remote-Sensing for Validation Studies) campaign, we combine HALO observations from a 35.2 GHz cloud and precipitation radar (1D) and from the hyperspectral 2D imager specMACS (Munich Aerosol Cloud Scanner), with a 30 times higher along-track resolution, and compare their cloud masks. We examine cloud size distributions in terms of sensitivity to sample size, resolution and the considered field of view (2D or 1D). This specifies impacts on horizontal cloud sizes derived from the across-track perspective of the high-resolution imager in comparison to the radar curtain. We assess whether and how the trade wind field amplifies uncertainties in cloud geometry observations along 1D transects through directional cloud elongation. Our findings reveal that each additional dimension, no matter of the device, causes a significant increase in observed clouds. The across-track field yields the highest increase in the cloud sample. The radar encounters difficulties in characterizing the trade wind cumuli size distribution. More than 60 % of clouds are subgrid scale for the radar. The radar has issues in the representation of clouds shorter than 200 m, as they are either unresolved or are incorrectly displayed as single grid points. Very shallow clouds can also remain unresolved due to too low radar sensitivity. Both facts deteriorate the cloud size distribution significantly at this scale. Double power law characteristics in the imager-based cloud size distribution do not occur in radar observations. Along-track measurements do not necessarily cover the predominant cloud extent and inferred geometries' lack of representativeness. Trade wind cumuli show horizontal patterns similar to ellipses, with a mean aspect ratio of 3:2 and having tendencies of stronger elongation with increasing cloud size. Instead of circular cloud shape estimations based on the 1D transect, elliptic fits maintain the cloud area size distribution. Increasing wind speed tends to stretch clouds more and tilts them into the wind field, which makes transect measurements more representative along this axis.
Xiaotong Li, Baozhu Wang, , Chao Wu
Atmospheric Measurement Techniques, Volume 15, pp 3629-3639;

The all-sky camera (ASC) images can reflect the local cloud cover information, and the cloud cover is one of the first factors considered for astronomical observatory site selection. Therefore, the realization of automatic classification of the ASC images plays an important role in astronomical observatory site selection. In this paper, three cloud cover features are proposed for the TMT (Thirty Meter Telescope) classification criteria, namely cloud weight, cloud area ratio and cloud dispersion. After the features are quantified, four classifiers are used to recognize the classes of the images. Four classes of ASC images are identified: “clear”, “inner”, “outer” and “covered”. The proposed method is evaluated on a large dataset, which contains 5000 ASC images taken by an all-sky camera located in Xinjiang (38.19 N, 74.53 E). In the end, the method achieves an accuracy of 96.58 % and F1_score of 96.24 % by a random forest (RF) classifier, which greatly improves the efficiency of automatic processing of the ASC images.
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