Development of a new nanoparticle sizer equipped with a 12-channel multi-port differential mobility analyzer and multi-condensation particle counters
Hong Ku Lee, Handol Lee, and Kang-Ho Ahn
Atmos. Meas. Tech., 13, 1551–1562, https://doi.org/10.5194/amt-13-1551-2020, 2020
We developed a nanoparticle sizer (NPS), consisting of a multi-port differential mobility analyzer (MP-DMA) with 12 sampling ports and multi-condensation particle counters (M-CPCs) for fast measurement of particle size distribution. The NPS can successfully capture the changes in particle size distribution under fast-changing particle concentration conditions. In this study, particle emissions from cooking activity are analyzed as an exemplary real-world application.
Cloud detection over snow and ice with oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC)
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, 2020
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables
Karl Lapo, Anita Freundorfer, Lena Pfister, Johann Schneider, John Selker, and Christoph Thomas
Atmos. Meas. Tech., 13, 1563–1573, https://doi.org/10.5194/amt-13-1563-2020, 2020
Most observations of the atmosphere are point observations, which only measure a small area around the sensor. This limitation creates problems for a number of disciplines, especially those that focus on how the surface and atmosphere exchange heat, mass, and momentum. We used distributed temperature sensing with fiber optics to demonstrate a key breakthrough in observing wind direction in a distributed way, i.e., not at a point, using small structures attached to the fiber-optic cables.
Development of a new nanoparticle sizer equipped with a 12-channel multi-port differential mobility analyzer and multi-condensation particle counters
Hong Ku Lee, Handol Lee, and Kang-Ho Ahn
Atmos. Meas. Tech., 13, 1551–1562, https://doi.org/10.5194/amt-13-1551-2020, 2020
We developed a nanoparticle sizer (NPS), consisting of a multi-port differential mobility analyzer (MP-DMA) with 12 sampling ports and multi-condensation particle counters (M-CPCs) for fast measurement of particle size distribution. The NPS can successfully capture the changes in particle size distribution under fast-changing particle concentration conditions. In this study, particle emissions from cooking activity are analyzed as an exemplary real-world application.
Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, 2020
A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Doppler lidar at Observatoire de Haute-Provence for wind profiling up to 75 km altitude: performance evaluation and observations
Sergey M. Khaykin, Alain Hauchecorne, Robin Wing, Philippe Keckhut, Sophie Godin-Beekmann, Jacques Porteneuve, Jean-Francois Mariscal, and Jerome Schmitt
Atmos. Meas. Tech., 13, 1501–1516, https://doi.org/10.5194/amt-13-1501-2020, 2020
The article presents a powerful atmospheric instrument based on a laser radar (lidar), capable of measuring horizontal wind velocity at a wide range of altitudes. In this study, we evaluate the performance of the wind lidar at Observatoire de Haute-Provence and demonstrate the application of its measurements for studies of atmospheric dynamical processes. Finally, we present an example of early validation of the ESA Aeolus space-borne wind lidar using its ground-based predecessor.
Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, 2020
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Real-time pollen monitoring using digital holography
Eric Sauvageat, Yanick Zeder, Kevin Auderset, Bertrand Calpini, Bernard Clot, Benoît Crouzy, Thomas Konzelmann, Gian Lieberherr, Fiona Tummon, and Konstantina Vasilatou
Atmos. Meas. Tech., 13, 1539–1550, https://doi.org/10.5194/amt-13-1539-2020, 2020
We present the first validation of the only operational automatic pollen monitoring system based on holography, the Swisens Poleno. The device produces real-time images of coarse aerosols, and by applying a machine learning algorithm we identify a range of pollen taxa with accuracy >90 %. The device was further validated in controlled chamber experiments to verify the counting ability and the performance of additional fluorescence measurements, which can further be used in pollen identification.
Applying Deep Learning to NASA MODIS Data to Create a Community Record of Marine Low Cloud Mesoscale Morphology
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-61,2020
Preprint under review for AMT (discussion: open, 0 comments)
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process large amount of satellite data. The algorithm will help low cloud researchers to better understand their mesoscale organiizations.
MAX-DOAS measurements of tropospheric NO2 and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations
Ka Lok Chan, Matthias Wiegner, Carlos Alberti, and Mark Wenig
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-35,2020
Preprint under review for AMT (discussion: open, 0 comments)
The manuscript presents 2D MAX-DOAS observations of vertical distribution of aerosol extinction, NO2 and HCHO in Munich. The measured surface aerosol extinction coefficients and NO2 mixing ratios are compared to in-situ monitor data. The NO2 and HCHO data are subsequently used to validate satellite measurements. The MAX-DOAS measurements are also used to investigate the spatio-temporal characteristic of NO2 and HCHO in Munich.
Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, 2020
A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Doppler lidar at Observatoire de Haute-Provence for wind profiling up to 75 km altitude: performance evaluation and observations
Sergey M. Khaykin, Alain Hauchecorne, Robin Wing, Philippe Keckhut, Sophie Godin-Beekmann, Jacques Porteneuve, Jean-Francois Mariscal, and Jerome Schmitt
Atmos. Meas. Tech., 13, 1501–1516, https://doi.org/10.5194/amt-13-1501-2020, 2020
The article presents a powerful atmospheric instrument based on a laser radar (lidar), capable of measuring horizontal wind velocity at a wide range of altitudes. In this study, we evaluate the performance of the wind lidar at Observatoire de Haute-Provence and demonstrate the application of its measurements for studies of atmospheric dynamical processes. Finally, we present an example of early validation of the ESA Aeolus space-borne wind lidar using its ground-based predecessor.
Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, 2020
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Real-time pollen monitoring using digital holography
Eric Sauvageat, Yanick Zeder, Kevin Auderset, Bertrand Calpini, Bernard Clot, Benoît Crouzy, Thomas Konzelmann, Gian Lieberherr, Fiona Tummon, and Konstantina Vasilatou
Atmos. Meas. Tech., 13, 1539–1550, https://doi.org/10.5194/amt-13-1539-2020, 2020
We present the first validation of the only operational automatic pollen monitoring system based on holography, the Swisens Poleno. The device produces real-time images of coarse aerosols, and by applying a machine learning algorithm we identify a range of pollen taxa with accuracy >90 %. The device was further validated in controlled chamber experiments to verify the counting ability and the performance of additional fluorescence measurements, which can further be used in pollen identification.
Applying Deep Learning to NASA MODIS Data to Create a Community Record of Marine Low Cloud Mesoscale Morphology
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-61,2020
Preprint under review for AMT (discussion: open, 0 comments)
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process large amount of satellite data. The algorithm will help low cloud researchers to better understand their mesoscale organiizations.
MAX-DOAS measurements of tropospheric NO2 and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations
Ka Lok Chan, Matthias Wiegner, Carlos Alberti, and Mark Wenig
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-35,2020
Preprint under review for AMT (discussion: open, 0 comments)
The manuscript presents 2D MAX-DOAS observations of vertical distribution of aerosol extinction, NO2 and HCHO in Munich. The measured surface aerosol extinction coefficients and NO2 mixing ratios are compared to in-situ monitor data. The NO2 and HCHO data are subsequently used to validate satellite measurements. The MAX-DOAS measurements are also used to investigate the spatio-temporal characteristic of NO2 and HCHO in Munich.
Validation of tropospheric NO2 column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations
Gaia Pinardi, Michel Van Roozendael, François Hendrick, Nicolas Theys, Nader Abuhassan, Alkiviadis Bais, Folkert Boersma, Alexander Cede, Jihyo Chong, Sebastian Donner, Theano Drosoglou, Udo Frieß, José Granville, Jay R. Herman, Henk Eskes, Robert Holla, Jari Hovila, Hitoshi Irie, Yugo Kanaya, Dimitris Karagkiozidis, Natalia Kouremeti, Jean-Christopher Lambert, Jianzhong Ma, Enno Peters, Ankie Piters, Oleg Postylyakov, Andreas Richter, Julia Remmers, Hisahiro Takashima, Martin Tiefengraber, Pieter Valks, Tim Vlemmix, Thomas Wagner, and Folkard Wittrock
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-76,2020
Preprint under review for AMT (discussion: open, 0 comments)
We validate several GOME-2 and OMI tropospheric NO2 products with 23 MAX-DOAS and 16 direct sun instruments distributed worldwide. We highlight large horizontal inhomogeneities at several sites affecting the validation results and we propose a method to quantify and correct for it. We show that the application of such correction reduces the satellite underestimation in almost all heterogeneous cases, but a negative bias remains over the MAX-DOAS + direct sun network ensemble for both satellites.
Testing the near-field Gaussian plume inversion flux quantification technique using unmanned aerial vehicle sampling
Adil Shah, Joseph R. Pitt, Hugo Ricketts, J. Brian Leen, Paul I. Williams, Khristopher Kabbabe, Martin W. Gallagher, and Grant Allen
Atmos. Meas. Tech., 13, 1467–1484, https://doi.org/10.5194/amt-13-1467-2020, 2020
Methane is a potent greenhouse gas, with large flux uncertainties from facility-scale sources, such as natural gas extraction infrastructure. A recently developed flux quantification method was successfully tested by flying an unmanned aerial vehicle (UAV) downwind of 22 controlled atmospheric methane releases. The UAVs were used to derive high-precision atmospheric methane measurements. The UAV methodology was successful in both detecting the release and providing a rough flux estimate.
InnFLUX – an open-source code for conventional and disjunct eddy covariance analysis of trace gas measurements: an urban test case
Marcus Striednig, Martin Graus, Tilmann D. Märk, and Thomas G. Karl
Atmos. Meas. Tech., 13, 1447–1465, https://doi.org/10.5194/amt-13-1447-2020, 2020
The current work summarizes a long-term effort to provide an open-source code for the analysis of turbulent fluctuations of trace gases in the atmosphere by eddy covariance and disjunct eddy covariance, with a special focus on reactive gases that participate in atmospheric chemistry. The performance of the code is successfully evaluated based on measurements of minute fluxes of non-methane volatile organic compounds into the urban atmosphere.
The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights
Debora Griffin, Christopher Sioris, Jack Chen, Nolan Dickson, Andrew Kovachik, Martin de Graaf, Swadhin Nanda, Pepijn Veefkind, Enrico Dammers, Chris A. McLinden, Paul Makar, and Ayodeji Akingunola
Atmos. Meas. Tech., 13, 1427–1445, https://doi.org/10.5194/amt-13-1427-2020, 2020
This study looks into validating the aerosol layer height product from the recently launched TROPOspheric Monitoring Instrument (TROPOMI) for forest fire plume through comparisons with two other satellite products, and interpreting differences due to the individual measurement techniques. These satellite observations are compared to predicted plume heights from Environment and Climate Change's air quality forecast model.
