Articles | Volume 13, issue 4
https://doi.org/10.5194/amt-13-1671-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1671-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Apr 2020
Research article |
| 06 Apr 2020
| 06 Apr 2020
Surface flux estimates derived from UAS-based mole fraction measurements by means of a nocturnal boundary layer budget approach
Max Planck Institute for Biogeochemistry, Jena, Germany
Jost V. Lavric
Max Planck Institute for Biogeochemistry, Jena, Germany
Rainer Gasche
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe
Institute of Technology, Garmisch-Partenkirchen, Germany
Christoph Gerbig
Max Planck Institute for Biogeochemistry, Jena, Germany
Richard H. Grant
Department of Agronomy, Purdue University, West Lafayette, IN, USA
Frank-Thomas Koch
Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeißenberg,
Germany
Marcus Schumacher
Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeißenberg,
Germany
now at: Alfred Wegener Institute, Helmholtz Centre for Polar and
Marine Research (AWI), Neumayer station III, Antarctica
Benjamin Wolf
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe
Institute of Technology, Garmisch-Partenkirchen, Germany
Matthias Zeeman
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe
Institute of Technology, Garmisch-Partenkirchen, Germany
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- Inverse problem for retrieving greenhouse gas fluxes at the non-uniform underlying surface from measurements of their concentrations at several levels I. Mukhartova et al. 10.1088/1742-6596/2701/1/012141
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- Environmental chemical sensing using small drones: A review J. Burgués & S. Marco 10.1016/j.scitotenv.2020.141172
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- Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6
- An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer I. Mukhartova et al. 10.3390/rs16132502
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- On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
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16 citations as recorded by crossref.
- Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements T. Andersen et al. 10.5194/acp-23-5191-2023
- Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement S. Koziel et al. 10.3390/s25082352
- Inverse problem for retrieving greenhouse gas fluxes at the non-uniform underlying surface from measurements of their concentrations at several levels I. Mukhartova et al. 10.1088/1742-6596/2701/1/012141
- Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
- Transition flight of a concept of lifting-wing quadcopter A. Daud Filho et al. 10.1017/aer.2024.100
- Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring A. Pietrenko-Dabrowska et al. 10.1016/j.measurement.2024.115168
- High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling S. Koziel et al. 10.1038/s41598-024-77214-y
- Environmental chemical sensing using small drones: A review J. Burgués & S. Marco 10.1016/j.scitotenv.2020.141172
- Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy S. Dimitriadou et al. 10.1016/j.scitotenv.2024.174966
- Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6
- An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer I. Mukhartova et al. 10.3390/rs16132502
- Ultra-Light Airborne Measurement System for Investigation of Urban Boundary Layer Dynamics P. Sekula et al. 10.3390/s21092920
- On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
- Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing S. Koziel et al. 10.1016/j.jestch.2024.101729
- Use of thermal signal for the investigation of near-surface turbulence M. Zeeman 10.5194/amt-14-7475-2021
- UAV-based in situ measurements of CO2 and CH4 fluxes over complex natural ecosystems A. Bolek et al. 10.5194/amt-17-5619-2024
Latest update: 09 May 2025
Short summary
The nocturnal boundary layer (NBL) budget method enables the quantification of gas fluxes between ecosystems and the atmosphere under nocturnal stable stratification, a condition under which standard approaches struggle. However, up to now the application of the NBL method has been limited by difficulties in obtaining the required measurements. We show how an unmanned aircraft system (UAS) equipped with a carbon dioxide analyser can make this method more accessible.
The nocturnal boundary layer (NBL) budget method enables the quantification of gas fluxes...
