Atmos. Meas. techniques

Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang, Heinrich Ruser, Julian Schade, Johannes Passig, Thomas Adam, Günther Dollinger, and Ralf Zimmermann
Atmos. Meas. Tech., 17, 299–313, https://doi.org/10.5194/amt-17-299-2024, 2024
This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.

Absolute radiance calibration in the UV and visible spectral range using atmospheric observations during twilight
Thomas Wagner and Jānis Puķīte
Atmos. Meas. Tech., 17, 277–297, https://doi.org/10.5194/amt-17-277-2024, 2024
We present a radiance calibration method based on the comparison of measurements and radiative transfer simulations of the zenith-scattered sun radiance during twilight. Cloud-free conditions are required. The method can be applied to measurements in the filed, and no laboratory measurements are required. The accuracy is estimated to range from about 4 % at 340 nm to about 10 % at 700 nm.

Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 247–259, https://doi.org/10.5194/amt-17-247-2024, 2024
Algorithms merge satellite radiometer data from various frequency channels, each tied to a different footprint size. We studied the uncertainty associated with sampling (over the Netherlands using 4 years of data) as precipitation is highly variable in space and time by simulating ground-based data as satellite footprints. Though sampling affects precipitation estimates, it doesn’t explain all discrepancies. Overall, uncertainties in the algorithm seem more influential than how data is sampled.

Airborne observation with a low-cost hyperspectral instrument: retrieval of NO2 vertical column densities (VCDs) and the satellite sub-grid variability over industrial point sources
Jong-Uk Park, Hyun-Jae Kim, Jin-Soo Park, Jinsoo Choi, Sang Seo Park, Kangho Bae, Jong-Jae Lee, Chang-Keun Song, Soojin Park, Kyuseok Shim, Yeonsoo Cho, and Sang-Woo Kim
Atmos. Meas. Tech., 17, 197–217, https://doi.org/10.5194/amt-17-197-2024, 2024
The high-spatial-resolution NO2 vertical column densities (VCDs) were measured from airborne observations using the low-cost hyperspectral imaging sensor (HIS) at three industrial areas in South Korea with the newly developed versatile NO2 VCD retrieval algorithm apt to be applied to the instruments with volatile optical and radiometric properties. The airborne HIS observations emphasized the intensifying satellite sub-grid variability in NO2 VCDs near the emission sources.

Measurement uncertainties of scanning microwave radiometers and their influence on temperature profiling
Tobias Böck, Bernhard Pospichal, and Ulrich Löhnert
Atmos. Meas. Tech., 17, 219–233, https://doi.org/10.5194/amt-17-219-2024, 2024
In this study, measurement uncertainties from microwave radiometers and their impact on temperature profiling are analyzed. These measurement uncertainties include horizontal inhomogeneities of the atmosphere, pointing errors or tilts of the instrument, physical obstacles which are in the line of sight of the radiometer, and radio frequency interferences. Impacts on temperature profiles from these uncertainties are usually small in real-life scenarios and when obstacles are far enough away.

A new power-law model for μ–Λ relationships in convective and stratiform rainfall
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024, 2024
A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ–Λ relation provides a better physical interpretation of the relationship coefficients.

Research of low-cost air quality monitoring models with different machine learning algorithms
Gang Wang, Chunlai Yu, Kai Guo, Haisong Guo, and Yibo Wang
Atmos. Meas. Tech., 17, 181–196, https://doi.org/10.5194/amt-17-181-2024, 2024
A low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM) and gas pollutants simultaneously. The performance of the different algorithms (RF, MLR, KNN, BP, GA-BP) with the parameters such as R2 and RMSE are compared and discussed. These measurements indicate the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.

Drone CO2 Measurements During the Tajogaite Volcanic Eruption
John Ericksen, Tobias Fischer, G. Matthew Fricke, Scott Nowicki, Nemesio Pérez, Pedro Hernández Pérez, Eleazar Padrón González, and Melanie Moses
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-229,2024
Preprint under review for AMT (discussion: open, 0 comments)
Volcanic eruptions emit significant quantities of carbon dioxide (CO2) to the atmosphere. We present a new method for directly determining the CO2 emission from a volcanic eruption using om La Palma Island, Spain using an unpiloted areal vehicle (UAV). We also collected samples of the emitted CO2 and analyzed their isotopic composition. Together with the emission rate the isotopic data provide valuable information on the state of volcanic activity and the potential evolution the eruption.

Comparisons and quality control of wind observations in a mountainous city using wind profile radar and the Aeolus satellite
Hua Lu, Min Xie, Wei Zhao, Bojun Liu, Tijian Wang, and Bingliang Zhuang
Atmos. Meas. Tech., 17, 167–179, https://doi.org/10.5194/amt-17-167-2024, 2024
Observations of vertical wind in regions with complex terrain are essential, but they are always sparse and have poor representation. Data verification and quality control are conducted on the wind profile radar and Aeolus wind products in this study, trying to compensate for the limitations of wind field observations. The results shed light on the comprehensive applications of multi-source wind profile data in complicated terrain regions with sparse ground-based wind observations.

Preface to the inter-journal special issue “RUSTED: Reducing Uncertainty in Soluble aerosol Trace Element Deposition”
Morgane M. G. Perron, Susanne Fietz, Douglas S. Hamilton, Akinori Ito, Rachel U. Shelley, and Mingjin Tang
Atmos. Meas. Tech., 17, 165–166, https://doi.org/10.5194/amt-17-165-2024, 2024
The solubility of vital and toxic trace elements delivered by the atmosphere determines their potential to fertilise or limit ocean productivity. A poor understanding of aeolian trace element solubility and the absence of a standard method to define this parameter hinder accurate model representation of the impact of atmospheric deposition on ocean productivity in a changing climate. The inter-journal special issue aims at “Reducing Uncertainty in Soluble aerosol Trace Element Deposition”.