Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system
Astrid Lampert, Falk Pätzold, Magnus O. Asmussen, Lennart Lobitz, Thomas Krüger, Thomas Rausch, Torsten Sachs, Christian Wille, Denis Sotomayor Zakharov, Dominik Gaus, Stephan Bansmer, and Ellen Damm
Atmos. Meas. Tech., 13, 1937–1952, https://doi.org/10.5194/amt-13-1937-2020, 2020
Methane has high climate warming potential. Sources of methane can be distinguished by the isotopic composition. To investigate the origin of methane, an airborne sampling system has been developed that can take air samples worldwide and at various altitudes. The article shows the performance of the overall system, from taking samples to laboratory analyses. As known methane source, a rewetted peatland site, was studied, and the vertical distribution of the isotopic composition is investigated.
SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation
Wanyi Xie, Dong Liu, Ming Yang, Shaoqing Chen, Benge Wang, Zhenzhu Wang, Yingwei Xia, Yong Liu, Yiren Wang, and Chaofang Zhang
Atmos. Meas. Tech., 13, 1953–1961, https://doi.org/10.5194/amt-13-1953-2020, 2020
Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.
Flow-induced errors in airborne in situ measurements of aerosols and clouds
Antonio Spanu, Maximilian Dollner, Josef Gasteiger, T. Paul Bui, and Bernadett Weinzierl
Atmos. Meas. Tech., 13, 1963–1987, https://doi.org/10.5194/amt-13-1963-2020, 2020
This study investigates how the airflow around wing-mounted instruments on fast-flying aircraft affects aerosol and cloud measurements. It combines airborne data with numerical simulations and shows that particle speed, particle concentration, and shape of water droplets are modified by the airflow. The proposed correction strategy for optical particle counters and optical array probes considers airflow effects and significantly reduces errors of derived ambient aerosol and cloud properties.
Flow-induced errors in airborne in situ measurements of aerosols and clouds
Antonio Spanu, Maximilian Dollner, Josef Gasteiger, T. Paul Bui, and Bernadett Weinzierl
Atmos. Meas. Tech., 13, 1963–1987, https://doi.org/10.5194/amt-13-1963-2020, 2020
This study investigates how the airflow around wing-mounted instruments on fast-flying aircraft affects aerosol and cloud measurements. It combines airborne data with numerical simulations and shows that particle speed, particle concentration, and shape of water droplets are modified by the airflow. The proposed correction strategy for optical particle counters and optical array probes considers airflow effects and significantly reduces errors of derived ambient aerosol and cloud properties.
Issues related to the retrieval of stratospheric-aerosol particle size information based on optical measurements
Christian von Savigny and Christoph G. Hoffmann
Atmos. Meas. Tech., 13, 1909–1920, https://doi.org/10.5194/amt-13-1909-2020, 2020
Stratospheric sulfate aerosols increase the Earth's planetary albedo and can lead to significant surface cooling, for example in the aftermath of volcanic eruptions. Their particle size distribution, important for physical and chemical effects of these aerosols, is still not fully understood. The present paper proposes an explanation for systematic differences in aerosol particle size retrieved from measurements made in different measurement geometries and reported in earlier studies.
A new lidar inversion method using a surface reference target applied to the backscattering coefficient and lidar ratio retrievals of a fog-oil plume at short range
Florian Gaudfrin, Olivier Pujol, Romain Ceolato, Guillaume Huss, and Nicolas Riviere
Atmos. Meas. Tech., 13, 1921–1935, https://doi.org/10.5194/amt-13-1921-2020, 2020
A new elastic lidar inversion equation is presented. It is based on the backscattering signal from a surface reference target rather than that from a volumetric layer of reference as is usually done. The method presented can be used in the case of airborne elastic lidar measurements or when the lidar–target line is horizontal. Also, a new algorithm is described to retrieve the lidar ratio and the backscattering coefficient of an aerosol plume without any a priori assumptions about the plume.
Issues related to the retrieval of stratospheric-aerosol particle size information based on optical measurements
Christian von Savigny and Christoph G. Hoffmann
Atmos. Meas. Tech., 13, 1909–1920, https://doi.org/10.5194/amt-13-1909-2020, 2020
Stratospheric sulfate aerosols increase the Earth's planetary albedo and can lead to significant surface cooling, for example in the aftermath of volcanic eruptions. Their particle size distribution, important for physical and chemical effects of these aerosols, is still not fully understood. The present paper proposes an explanation for systematic differences in aerosol particle size retrieved from measurements made in different measurement geometries and reported in earlier studies.
A new lidar inversion method using a surface reference target applied to the backscattering coefficient and lidar ratio retrievals of a fog-oil plume at short range
Florian Gaudfrin, Olivier Pujol, Romain Ceolato, Guillaume Huss, and Nicolas Riviere
Atmos. Meas. Tech., 13, 1921–1935, https://doi.org/10.5194/amt-13-1921-2020, 2020
A new elastic lidar inversion equation is presented. It is based on the backscattering signal from a surface reference target rather than that from a volumetric layer of reference as is usually done. The method presented can be used in the case of airborne elastic lidar measurements or when the lidar–target line is horizontal. Also, a new algorithm is described to retrieve the lidar ratio and the backscattering coefficient of an aerosol plume without any a priori assumptions about the plume.
Simultaneous detection of ozone and nitrogen dioxide by oxygen anion chemical ionization mass spectrometry: a fast-time-response sensor suitable for eddy covariance measurements
Gordon A. Novak, Michael P. Vermeuel, and Timothy H. Bertram
Atmos. Meas. Tech., 13, 1887–1907, https://doi.org/10.5194/amt-13-1887-2020, 2020
We present the development and successful field deployment of a new chemical ionization mass spectrometry method capable of fast and high-sensitivity measurements of ozone and nitrogen dioxide in the atmosphere. The sensitivity, precision, and time resolution of the instrument were demonstrated to be sufficient for making deposition flux measurements of ozone from a coastal ocean field site. We propose this instrument will also be well suited for sampling from mobile platforms.
The quantification of NOx and SO2 point source emission flux errors of mobile DOAS on the basis of the Gaussian dispersion model: A simulation study
Yeyuan Huang, Ang Li, Thomas Wagner, Yang Wang, Zhaokun Hu, Pinhua Xie, Jin Xu, Hongmei Ren, Xiaoyi Fang, and Bing Dang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-81,2020
Preprint under review for AMT (discussion: open, 0 comments)
Mobile DOAS has become an important tool for the quantification of emission sources. In this study, we focused on the error budget of mobile DOAS measurements from point sources based on the model simulations, and we also offered recommendations for the optimum settings of such measurements. From the results, we also discovered a missing error source(undetectable flux) and clarified the [NOx]/[NO2] ratio correction effect of flux measurement.
Simultaneous detection of ozone and nitrogen dioxide by oxygen anion chemical ionization mass spectrometry: a fast-time-response sensor suitable for eddy covariance measurements
Gordon A. Novak, Michael P. Vermeuel, and Timothy H. Bertram
Atmos. Meas. Tech., 13, 1887–1907, https://doi.org/10.5194/amt-13-1887-2020, 2020
We present the development and successful field deployment of a new chemical ionization mass spectrometry method capable of fast and high-sensitivity measurements of ozone and nitrogen dioxide in the atmosphere. The sensitivity, precision, and time resolution of the instrument were demonstrated to be sufficient for making deposition flux measurements of ozone from a coastal ocean field site. We propose this instrument will also be well suited for sampling from mobile platforms.
The quantification of NOx and SO2 point source emission flux errors of mobile DOAS on the basis of the Gaussian dispersion model: A simulation study
Yeyuan Huang, Ang Li, Thomas Wagner, Yang Wang, Zhaokun Hu, Pinhua Xie, Jin Xu, Hongmei Ren, Xiaoyi Fang, and Bing Dang
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-81,2020
Preprint under review for AMT (discussion: open, 0 comments)
Mobile DOAS has become an important tool for the quantification of emission sources. In this study, we focused on the error budget of mobile DOAS measurements from point sources based on the model simulations, and we also offered recommendations for the optimum settings of such measurements. From the results, we also discovered a missing error source(undetectable flux) and clarified the [NOx]/[NO2] ratio correction effect of flux measurement.
Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018
Stuart K. Grange, Hanspeter Lötscher, Andrea Fischer, Lukas Emmenegger, and Christoph Hueglin
Atmos. Meas. Tech., 13, 1867–1885, https://doi.org/10.5194/amt-13-1867-2020, 2020
Black carbon (BC) is an important atmospheric pollutant and can be monitored by instruments called aethalometers. A pragmatic data processing technique called the aethalometer model can be used to apportion aethalometer observations into traffic and woodburning components. We present an exploratory data analysis evaluating the aethalometer model and use the outputs for BC trend analysis across Switzerland. The aethalometer model's robustness and utility for such analyses is discussed.
Intra-annual variations of spectrally resolved gravity wave activity in the UMLT region
René Sedlak, Alexandra Zuhr, Carsten Schmidt, Sabine Wüst, Michael Bittner, Goderdzi G. Didebulidze, and Colin Price
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-14,2020
Preprint under review for AMT (discussion: open, 0 comments)
Gravity wave (GW) activity in the UMLT in the period range 6–480 min is calculated by applying a wavelet analysis to nocturnal temperature time series derived from OH* airglow spectrometers. We analyse measurements from eight different locations at different latitudes.
GW activity shows strong period dependence. We find hardly any seasonal variability for periods below 60 min and a semi-annual cycle for periods longer than 60 min that evolves into an annual cycle around a period of 200 min.
Leveraging spatial textures, through machine learning, to identify aerosol and distinct cloud types from multispectral observations
Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-74,2020
Preprint under review for AMT (discussion: open, 0 comments)
Space agencies use moderate resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impacts the Earth's climate; these space agencies include NASA, ESA, China Meteorological Administration, etc. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement compared current aerosol and cloud detection algorithms.
Nano-hygroscopicity tandem differential mobility analyzer (nano-HTDMA) for investigating hygroscopic properties of sub-10 nm aerosol nanoparticles
Ting Lei, Nan Ma, Juan Hong, Thomas Tuch, Xin Wang, Zhibin Wang, Mira Pöhlker, Maofa Ge, Weigang Wang, Eugene Mikhailov, Thorsten Hoffmann, Ulrich Pöschl, Hang Su, Alfred Wiedensohler, and Yafang Cheng
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-110,2020
Preprint under review for AMT (discussion: open, 0 comments)
We present a newly designed and self-assembled nano-hygroscopicity tandem differential mobility analyzer (nano-HTDMA) apparatus that enables high accuracy and precision in hygroscopic growth measurements of aerosol nanoparticles with diameters less than 10 nm. We further introduce the comprehensive methods for system calibration and performance of the system.
Intercomparison and Evaluation of Ground- and Satellite-Based Stratospheric Ozone and Temperature profiles above Observatoire Haute Provence during the Lidar Validation NDACC Experiment (LAVANDE)
Robin Wing, Wolfgang Steinbrecht, Sophie Godin-Beekmann, Thomas J. McGee, John T. Sullivan, Grant Sumnicht, Gerard Ancellet, Alain Hauchecorne, Sergey Khaykin, and Philippe Keckhut
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-9,2020
Preprint under review for AMT (discussion: open, 0 comments)
A lidar intercomparison campaign was conducted over a period of 28 nights at L'Observatoire de Haute Provence (OHP) in 2017 and 2018. The objective is to validate the ozone and temperature profiles at OHP to ensure the quality of the data submitted to the NDACC database remains high. A mobile reference lidar operated by NASA was transported to OHP and operated concurrently with the French lidars. Agreement for ozone was better than 5 % between 20 and 40 km and temperatures were equal within 3 K.
Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018
Stuart K. Grange, Hanspeter Lötscher, Andrea Fischer, Lukas Emmenegger, and Christoph Hueglin
Atmos. Meas. Tech., 13, 1867–1885, https://doi.org/10.5194/amt-13-1867-2020, 2020
Black carbon (BC) is an important atmospheric pollutant and can be monitored by instruments called aethalometers. A pragmatic data processing technique called the aethalometer model can be used to apportion aethalometer observations into traffic and woodburning components. We present an exploratory data analysis evaluating the aethalometer model and use the outputs for BC trend analysis across Switzerland. The aethalometer model's robustness and utility for such analyses is discussed.
Intra-annual variations of spectrally resolved gravity wave activity in the UMLT region
René Sedlak, Alexandra Zuhr, Carsten Schmidt, Sabine Wüst, Michael Bittner, Goderdzi G. Didebulidze, and Colin Price
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-14,2020
Preprint under review for AMT (discussion: open, 0 comments)
Gravity wave (GW) activity in the UMLT in the period range 6–480 min is calculated by applying a wavelet analysis to nocturnal temperature time series derived from OH* airglow spectrometers. We analyse measurements from eight different locations at different latitudes.
GW activity shows strong period dependence. We find hardly any seasonal variability for periods below 60 min and a semi-annual cycle for periods longer than 60 min that evolves into an annual cycle around a period of 200 min.
Leveraging spatial textures, through machine learning, to identify aerosol and distinct cloud types from multispectral observations
Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-74,2020
Preprint under review for AMT (discussion: open, 0 comments)
Space agencies use moderate resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impacts the Earth's climate; these space agencies include NASA, ESA, China Meteorological Administration, etc. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement compared current aerosol and cloud detection algorithms.
