Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1743-2021
© Author(s) 2021. 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-14-1743-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar
Xiaoyu Hu
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jinming Ge
CORRESPONDING AUTHOR
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jiajing Du
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qinghao Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jianping Huang
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qiang Fu
Department of Atmospheric Sciences, University of Washington, Seattle, WA 98105, USA
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Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes, though gap-filling CO2 fluxes is challenging at dryland sites due to small fluxes. Using data collected from a semiarid site, four machine learning methods are evaluated with different lengths of artificial gaps. The artificial neural network and random forest methods outperform the other methods. With these methods, uncertainties in the annual CO2 flux at this site are estimated to be within 16 g C m−2.
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Short summary
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutter. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutter by considering their different features in several variables. We applied this method to the radar observations at the SACOL site and found the results have good agreement with lidar detection.
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar...