Atmospheric Measurement Techniques

Journal: Atmospheric Measurement Techniques

Abbreviation

Atmos. meas. tech.

Publisher

Copernicus

ISSN

1867-1381
1867-8548

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Publications1 - 10 of 106

Development, characterization and first deployment of an improved online reactive oxygen species analyzer
Zhou, Jun; Bruns, Emily A.; Zotter, Peter; et al. (2018)
Atmospheric Measurement Techniques
Inhalation of atmospheric particles is linked to human diseases. Reactive oxygen species (ROS) present in these atmospheric aerosols may play an important role. However, the ROS content in aerosols and their formation pathways are still largely unknown. Here, we have developed an online and offline ROS analyzer using a 2′,7′-dichlorofluorescin (DCFH) based assay. The ROS analyzer was calibrated with H2O2 and its sensitivity was characterized using a suite of model organic compounds. The instrument detection limit determined as 3 times the noise is 1.3nmolL−1 for offline analysis and 2nmolm−3 of sampled air when the instrument is operated online at a fluorescence response time of approximately 8min, while the offline method detection limit is 18nmolL−1. Potential interferences from gas-phase O3 and NO2 as well as matrix effects of particulate SO42− and NO3− were tested, but not observed. Fe3+ had no influence on the ROS signal, while soluble Fe2+ reduced it if present at high concentrations in the extracts. Both online and offline methods were applied to identify the ROS content of different aerosol types, i.e., ambient aerosols as well as fresh and aged aerosols from wood combustion emissions. The stability of the ROS was assessed by comparing the ROS concentration measured by the same instrumentation online in situ with offline measurements. We also analyzed the evolution of ROS in specific samples by conducting the analysis after storage times of up to 4 months. The ROS were observed to decay with increasing storage duration. From their decay behavior, ROS in secondary organic aerosol (SOA) can be separated into short- and long-lived fractions. The half-life of the short-lived fraction was 1.7±0.4h, while the half-life of the long-lived fraction could not be determined with our uncertainties. All these measurements showed consistently that on average 60±20% of the ROS were very reactive and disappeared during the filter storage time. This demonstrates the importance of a fast online measurement of ROS.
A convolutional neural network for classifying cloud particles recorded by imaging probes
Touloupas, Georgios; Lauber, Annika; Henneberger, Jan; et al. (2020)
Atmospheric Measurement Techniques
During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an automated classification approach. Traditional classification methods that require extracting features manually (e.g., decision trees and support vector machines) show reasonable performance when trained and tested on data coming from a unique dataset. However, they often have difficulties in generalizing to test sets coming from other datasets where the distribution of the features might be significantly different. In practice, we found that for holographic imagers each new dataset requires labeling a huge amount of data by hand using those methods. Convolutional neural networks have the potential to overcome this problem due to their ability to learn complex nonlinear models directly from the images instead of pre-engineered features, as well as by relying on powerful regularization techniques. We show empirically that a convolutional neural network trained on cloud particles from holographic imagers generalizes well to unseen datasets. Moreover, fine tuning the same network with a small number (256) of training images improves the classification accuracy. Thus, the automated classification with a convolutional neural network not only reduces the hand-labeling effort for new datasets but is also no longer the main error source for the classification of small particles.
A reference data set for validating vapor pressure measurement techniques: Homologous series of polyethylene glycols
Krieger, Ulrich K.; Siegrist, Franziska; Marcolli, Claudia; et al. (2018)
Atmospheric Measurement Techniques
To predict atmospheric partitioning of organic compounds between gas and aerosol particle phase based on explicit models for gas phase chemistry, saturation vapor pressures of the compounds need to be estimated. Estimation methods based on functional group contributions require training sets of compounds with well-established saturation vapor pressures. However, vapor pressures of semivolatile and low-volatility organic molecules at atmospheric temperatures reported in the literature often differ by several orders of magnitude between measurement techniques. These discrepancies exceed the stated uncertainty of each technique which is generally reported to be smaller than a factor of 2. At present, there is no general reference technique for measuring saturation vapor pressures of atmospherically relevant compounds with low vapor pressures at atmospheric temperatures. To address this problem, we measured vapor pressures with different techniques over a wide temperature range for intercomparison and to establish a reliable training set. We determined saturation vapor pressures for the homologous series of polyethylene glycols (H − (O − CH2 − CH2)n − OH) for n = 3 to n = 8 ranging in vapor pressure at 298 K from 10−7 to 5×10−2 Pa and compare them with quantum chemistry calculations. Such a homologous series provides a reference set that covers several orders of magnitude in saturation vapor pressure, allowing a critical assessment of the lower limits of detection of vapor pressures for the different techniques as well as permitting the identification of potential sources of systematic error. Also, internal consistency within the series allows outlying data to be rejected more easily. Most of the measured vapor pressures agreed within the stated uncertainty range. Deviations mostly occurred for vapor pressure values approaching the lower detection limit of a technique. The good agreement between the measurement techniques (some of which are sensitive to the mass accommodation coefficient and some not) suggests that the mass accommodation coefficients of the studied compounds are close to unity. The quantum chemistry calculations were about 1 order of magnitude higher than the measurements. We find that extrapolation of vapor pressures from elevated to atmospheric temperatures is permissible over a range of about 100 K for these compounds, suggesting that measurements should be performed best at temperatures yielding the highest-accuracy data, allowing subsequent extrapolation to atmospheric temperatures.
The Microfluidic Ice Nuclei Counter Zurich (MINCZ): a platform for homogeneous and heterogeneous ice nucleation
Isenrich, Florin N.; Shardt, Nadia; Rösch, Michael; et al. (2022)
Atmospheric Measurement Techniques
Ice nucleation in the atmosphere is the precursor to important processes that determine cloud properties and lifetime. Computational models that are used to predict weather and project future climate changes require parameterizations of both homogeneous nucleation (i.e. in pure water) and heterogeneous nucleation (i.e. catalysed by ice-nucleating particles, INPs). Microfluidic systems have gained momentum as a tool for obtaining such parameterizations and gaining insight into the stochastic and deterministic contributions to ice nucleation. To overcome the shortcomings of polydimethylsiloxane (PDMS) microfluidic devices with regard to temperature uncertainty and droplet instability due to continuous water adsorption by PDMS, we have developed a new instrument: the Microfluidic Ice Nuclei Counter Zurich (MINCZ). In MINCZ, droplets with a diameter of 75 mu m are generated using a PDMS chip, and hundreds of these droplets are then stored in fluoropolymer tubing that is relatively impermeable to water and solvents. Droplets within the tubing are cooled in an ethanol bath. We validate MINCZ by measuring the homogeneous freezing temperatures of water droplets and the heterogeneous freezing temperatures of aqueous suspensions containing microcline, a common and effective INP in the atmosphere. We obtain results with a high accuracy of 0.2 K in measured droplet temperature. Pure water droplets with a diameter of 75 mu m freeze at a median temperature of 237.3 K with a standard deviation of 0.1 K. Additionally, we perform several freeze-thaw cycles. In the future, MINCZ will be used to investigate the freezing behaviour of INPs, motivated by a need for better-constrained parameterizations of ice nucleation in weather and climate models, wherein the presence or absence of ice influences cloud optical properties and precipitation formation.
Performance of a low-cost methane sensor for ambient concentration measurements in preliminary studies
Eugster, Werner; Kling, G. W. (2012)
Atmospheric Measurement Techniques
Methane is the second most important greenhouse gas after CO2 and contributes to global warming. Its sources are not uniformly distributed across terrestrial and aquatic ecosystems, and most of the methane flux is expected to stem from hotspots which often occupy a very small fraction of the total landscape area. Continuous time-series measurements of CH4 concentrations can help identify and locate these methane hotspots. Newer, low-cost trace gas sensors such as the Figaro TGS 2600 can detect CH4 even at ambient concentrations. Hence, in this paper we tested this sensor under real-world conditions over Toolik Lake, Alaska, to determine its suitability for preliminary studies before placing more expensive and service-intensive equipment at a given locality. A reasonably good agreement with parallel measurements made using a Los Gatos Research FMA 100 methane analyzer was found after removal of the strong sensitivities for temperature and relative humidity. Correcting for this sensitivity increased the absolute accuracy required for in-depth studies, and the reproducibility between two TGS 2600 sensors run in parallel is very good. We conclude that the relative CH4 concentrations derived from such sensors are sufficient for preliminary investigations in the search of potential methane hotspots.
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland
Aichinger-Rosenberger, Matthias; Brockmann, Elmar; Crocetti, Laura; et al. (2022)
Atmospheric Measurement Techniques
Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %-80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.
A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements
Nyamsi, William Wandji; Lipponen, Antti; Sanchez-Lorenzo, Arturo; et al. (2020)
Atmospheric Measurement Techniques
A novel method has been developed to estimate aerosol optical depth (AOD) from sunshine duration (SD) measurements under cloud-free conditions. It is a physically based method serving for the reconstruction of the historical evolution of AOD during the last century. In addition to sunshine duration data, it requires daily water vapor and ozone products as inputs taken from the ECMWF 20th century reanalysis ERA-20C, available at the global scale over the period 1900–2010. Surface synoptic cloud observations are used to identify cloud-free days. For 16 sites over Europe, the accuracy of the estimated daily AOD, and its seasonal variability, is similar to or better than those from two earlier methods when compared to AErosol RObotic NETwork measurements. In addition, it also improves the detection of the signal from massive aerosol events such as important volcanic eruptions (e.g., Arenal and Fernandina Island in 1968, El Chichón in 1982 and Pinatubo in 1992). Finally, the reconstructed AOD time series are in good agreement with the dimming/brightening phenomenon and also provide preliminary evidence of the early-brightening phenomenon.
Using a holographic imager on a tethered balloon system for microphysical observations of boundary layer clouds
Ramelli, Fabiola; Beck, Alexander; Henneberger, Jan; et al. (2020)
Atmospheric Measurement Techniques
Conventional techniques to measure boundary layer clouds such as research aircraft are unable to sample in orographically diverse or densely populated areas. In this paper, we present a newly developed measurement platform on a tethered balloon system (HoloBalloon) to measure in situ vertical profiles of microphysical and meteorological cloud properties up to 1 km above ground. The main component of the HoloBalloon platform is a holographic imager, which uses digital in-line holography to image an ensemble of cloud particles in the size range from small cloud droplets to precipitation-sized particles in a three-dimensional volume. Based on a set of two-dimensional images, information about the phase-resolved particle size distribution, shape and spatial distribution can be obtained. The velocity-independent sample volume makes holographic imagers particularly well suited for measurements on a balloon. The unique combination of holography and balloon-borne measurements allows for observations with high spatial resolution, covering cloud structures from the kilometer down to the millimeter scale. The potential of the measurement technique in studying boundary layer clouds is demonstrated on the basis of a case study. We present observations of a supercooled low stratus cloud during a Bise situation over the Swiss Plateau in February 2018. In situ microphysical profiles up to 700 m altitude above the ground were performed at temperatures down to −8 ∘C and wind speeds up to 15 m s−1. We were able to capture unique microphysical signatures in stratus clouds, in the form of inhomogeneities in the cloud droplet number concentration and in cloud droplet size, from the kilometer down to the meter scale.
Two new multirotor uncrewed aerial vehicles (UAVs) for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project
Miller, Anna J.; Ramelli, Fabiola; Fuchs, Christopher; et al. (2024)
Atmospheric Measurement Techniques
Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community.
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Pan, Yuanxin; Kłopotek, Grzegorz; Crocetti, Laura; et al. (2024)
Atmospheric Measurement Techniques
The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

Publications1 - 10 of 106

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