Atmos. Meas. techniques

Two new multirotor uncrewed aerial vehicles (UAVs) for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project
Anna J. Miller, Fabiola Ramelli, Christopher Fuchs, Nadja Omanovic, Robert Spirig, Huiying Zhang, Ulrike Lohmann, Zamin A. Kanji, and Jan Henneberger
Atmos. Meas. Tech., 17, 601–625, https://doi.org/10.5194/amt-17-601-2024, 2024
We present a method for aerosol and cloud research using two uncrewed aerial vehicles (UAVs). The UAVs have a propeller heating mechanism that allows flights in icing conditions, which has so far been a limitation for cloud research with UAVs. One UAV burns seeding flares, producing a plume of particles that causes ice formation in supercooled clouds. The second UAV measures aerosol size distributions and is used for measuring the seeding plume or for characterizing the boundary layer.

Mispointing characterization and Doppler velocity correction for the conically scanning WIVERN Doppler radar
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech., 17, 499–514, https://doi.org/10.5194/amt-17-499-2024, 2024
The WIVERN mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at providing measurements of horizontal winds in cloud and precipitation systems through a conically scanning W-band Doppler radar. This work discusses four methods that can be used to characterize and correct the Doppler velocity error induced by the antenna mispointing. The proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
Atmos. Meas. Tech., 17, 515–538, https://doi.org/10.5194/amt-17-515-2024, 2024
The latest version of the GPROF retrieval algorithm that produces global precipitation estimates using observations from the Global Precipitation Measurement mission is validated against ground-based radars. The validation shows that the algorithm accurately estimates precipitation on scales ranging from continental to regional. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.

First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024, 2024
This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm
James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang
Atmos. Meas. Tech., 17, 471–498, https://doi.org/10.5194/amt-17-471-2024, 2024
We present the new Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that fuses observations from GOES-16 and GOES-17 to retrieve information about aerosol loading (at 10–15 min cadence) and aerosol particle properties (daily), all at pixel-level resolution. We present MAGARA results for three case studies: the 2018 California Camp Fire, the 2019 Williams Flats Fire, and the 2019 Kincade Fire. We also compare MAGARA aerosol loading and particle properties with AERONET.

Increasing Aerosol Optical Depth Spatial And Temporal Availability By Merging Datasets from Geostationary And Sun-Synchronous Satellites
Pawan Gupta, Robert C. Levy, Shana Mattoo, Lorraine Remer, Zhaohui Zhang, Virginia Sawyer, Jennifer Wei, Sally Zhao, Min Oo, V. Praju Kiliyanpilakkil, and Xiaohua Pan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-259,2024
Preprint under review for AMT (discussion: open, 0 comments)
In this study, for the first time, we combined aerosol data from six satellites using a unified algorithm. The global datasets are generated at a high spatial resolution of about 25 km with an interval of 30 minutes. The new datasets are compared against ground truth and verified. They will be useful for various applications such as air quality monitoring, climate research, pollution diurnal variability, long-range smoke and dust transport, and evaluation of regional and global models.

Real-time pollen identification using holographic imaging and fluorescence measurements
Sophie Erb, Elias Graf, Yanick Zeder, Simone Lionetti, Alexis Berne, Bernard Clot, Gian Lieberherr, Fiona Tummon, Pascal Wullschleger, and Benoît Crouzy
Atmos. Meas. Tech., 17, 441–451, https://doi.org/10.5194/amt-17-441-2024, 2024
In this study, we focus on an automatic bioaerosol measurement instrument and investigate the impact of using its fluorescence measurement for pollen identification. The fluorescence signal is used together with a pair of images from the same instrument to identify single pollen grains via neural networks. We test whether considering fluorescence as a supplementary input improves the pollen identification performance by comparing three different neural networks.

A Correction Algorithm for Propeller-Induced Airflow and Flight Attitude Changes during Three-Dimensional Wind Speed Measurements Made from A Rotary Unmanned Aerial Vehicle
Yanrong Yang, Yuheng Zhang, Tianran Han, Conghui Xie, Yayong Liu, Yufei Huang, Jietao Zhou, Haijiong Sun, Delong Zhao, Kui Zhang, and Shao-Meng Li
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-248,2024
Preprint under review for AMT (discussion: open, 0 comments)
The paper introduces a correction algorithm for accurate wind speed measurement in a multirotor unmanned aerial vehicle (UAV) with a sonic anemometer. Addressing propeller rotation, UAV movement, and attitude changes, it integrates computational fluid dynamics simulation and regression analysis. This comprehensive algorithm corrects rotor disturbances, motion, and attitude variations. Validation against meteorological tower data demonstrates its enhanced reliability in wind speed measurements.

ampycloud: an algorithm to characterize cloud layers above aerodromes using ceilometer measurements
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, and Pieter du Preez
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-254,2024
Preprint under review for AMT (discussion: open, 0 comments)
ampycloud is new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer measurements. This algorithm was devised as part of a larger effort to fully automate the creation of Meteorological Aerodrome Reports (METARs) at Swiss civil airports. The ampycloud algorithm was implemented as a Python package, that is made publicly available to the community under the 3-Clause BSD license.

Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
Xinlei Ge, Yele Sun, Justin Trousdell, Mindong Chen, and Qi Zhang
Atmos. Meas. Tech., 17, 423–439, https://doi.org/10.5194/amt-17-423-2024, 2024
This study aims to enhance the application of the Aerodyne high-resolution aerosol mass spectrometer (HR-AMS) in characterizing organic nitrogen (ON) species within aerosol particles and droplets. A thorough analysis was conducted on 75 ON standards that represent a diverse spectrum of ambient ON types. The results underscore the capacity of the HR-AMS in examining the concentration and chemistry of atmospheric ON compounds, thereby offering insights into their sources and environmental impacts.

Performance Evaluation of MeteoTracker Mobile Sensor for Outdoor Applications
Francesco Barbano, Erika Brattich, Carlo Cintolesi, Abdul Ghafoor Nizamani, Silvana Di Sabatino, Massimo Milelli, Esther E. M. Peerlings, Sjoerd Polder, Gert-Jan Steeneveld, and Antonio Parodi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-256,2024
Preprint under review for AMT (discussion: open, 0 comments)
The characterization of the urban microclimate starts with atmospheric monitoring using a dense array of sensors, to capture the spatial variations induced by the different morphology, land cover and presence of vegetation. To provide a new sensor for this scope, this paper evaluates the outdoor performance of a commercial mobile sensor. The results mark the sensor's ability to capture the same atmospheric variability as the reference, making it a valid solution for atmospheric monitoring.

Radar and environment-based hail damage estimates using machine learning
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, 2024
The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regard.

Evaluation of the first year of Pandora NO2 measurements over Beijing and application to satellite validation
Ouyang Liu, Zhengqiang Li, Yangyan Lin, Cheng Fan, Ying Zhang, Kaitao Li, Peng Zhang, Yuanyuan Wei, Tianzeng Chen, Jiantao Dong, and Gerrit de Leeuw
Atmos. Meas. Tech., 17, 377–395, https://doi.org/10.5194/amt-17-377-2024, 2024
Nitrogen dioxide (NO2) is a trace gas which is important for atmospheric chemistry and may affect human health. To understand processes leading to harmful concentrations, it is important to monitor NO2 concentrations near the surface and higher up. To this end, a Pandora instrument has been installed in Beijing. An overview of the first year of data shows the large variability on diurnal to seasonal timescales and how this is affected by wind speed and direction and chemistry.

Multi-section reference value for the analysis of horizontally scanning aerosol lidar observations
Juseon Shin, Gahyeong Kim, Dukhyeon Kim, Matthias Tesche, Gahyeon Park, and Youngmin Noh
Atmos. Meas. Tech., 17, 397–406, https://doi.org/10.5194/amt-17-397-2024, 2024
We introduce the multi-section method, a novel approach for stable extinction coefficient retrievals in horizontally scanning aerosol lidar measurements, in this study. Our method effectively removes signal–noise-induced irregular peaks and derives a reference extinction coefficient, αref, from multiple scans, resulting in a strong correlation (>0.74) with PM2.5 mass concentrations. Case studies demonstrate its utility in retrieving spatio-temporal aerosol distributions and PM2.5 concentrations.

Measurements of atmospheric C10–C15 biogenic volatile organic compounds (BVOCs) with sorbent tubes
Heidi Hellén, Toni Tykkä, Simon Schallhart, Evdokia Stratigou, Thérèse Salameh, and Maitane Iturrate-Garcia
Atmos. Meas. Tech., 17, 315–333, https://doi.org/10.5194/amt-17-315-2024, 2024
Even though online measurements of biogenic volatile organic compounds (BVOCs) are becoming more common, the use of sorbent tubes is expected to continue because they offer greater spatial coverage and no infrastructure is required for sampling. In this study the sorbent tube sampling method was optimized and evaluated for the determination of BVOCs in gas-phase samples. Tenax TA sorbent tubes were found to be suitable for the quantitative measurements of C10–C15 BVOCs.

Measuring diameters and velocities of artificial raindrops with a neuromorphic event camera
Kire Micev, Jan Steiner, Asude Aydin, Jörg Rieckermann, and Tobi Delbruck
Atmos. Meas. Tech., 17, 335–357, https://doi.org/10.5194/amt-17-335-2024, 2024
This paper reports a novel rain droplet measurement method that uses a neuromorphic event camera to measure droplet sizes and speeds as they fall through a shallow plane of focus. Experimental results report accuracy similar to a commercial laser sheet disdrometer. Because these measurements are driven by event camera activity, this approach could enable the economical deployment of ubiquitous networks of solar-powered disdrometers.

Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Soheila Jafariserajehlou, Sachin S. Gunthe, Andreas Herber, Christoph Ritter, Hartmut Bösch, and John P. Burrows
Atmos. Meas. Tech., 17, 359–375, https://doi.org/10.5194/amt-17-359-2024, 2024
Aerosols are suspensions of particles dispersed in the air. In this study, we use a novel retrieval of satellite data to investigate an optical property of aerosols, the aerosol optical depth, in the high Arctic to assess their direct and indirect roles in climate change. This study demonstrates that the presented approach shows good quality and very promising potential.

Regional validation of the solar irradiance tool SolaRes in clear-sky conditions, with a focus on the aerosol module
Thierry Elias, Nicolas Ferlay, Gabriel Chesnoiu, Isabelle Chiapello, and Mustapha Moulana
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-236,2024
Preprint under review for AMT (discussion: open, 0 comments)
In the solar energy application field, it is important to simulate solar resource anywhere on the globe. We conceived the SolaRes tool to provide precise and accurate estimates of solar resource estimates for any solar plant technology. The paper presents the validation of SolaRes, by comparing estimates with measurements made on two ground-based platforms. Little differences are found, validating SolaRes. Validation is performed in clear-sky conditions when aerosols are main factors.

Atmospheric H2 observations from the NOAA Global Cooperative Air Sampling Network
Gabrielle B. Petron, Andrew M. Crotwell, John Mund, Molly Crotwell, Thomas Mefford, Kirk Thoning, Bradley D. Hall, Duane R. Kitzis, Monica Madronich, Eric Moglia, Don Neff, Sonja Wolter, Armin Jordan, Paul Krummel, Ray Langenfelds, and John D. Patterson
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-4,2024
Preprint under review for AMT (discussion: open, 0 comments)
Hydrogen, (H2) is a gas in trace amounts in the Earth’s atmosphere with indirect impacts on climate and air quality. Renewed interest in H2 as a low or zero carbon source of energy may lead to increased production, uses and supply chain emissions. NOAA measurements starting in 2009 were reprocessed to be on an internationally recognized H2 calibration scale. Time records from 70 sites in mostly remote locations complement other datasets to study H2 sources and sinks and their variability.

Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, 2024
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.