Articles | Volume 12, issue 8
https://doi.org/10.5194/amt-12-4591-2019
© Author(s) 2019. 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-12-4591-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2019
Research article |
| 30 Aug 2019
| 30 Aug 2019
Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm
Heike Kalesse
CORRESPONDING AUTHOR
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Teresa Vogl
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Cosmin Paduraru
Department of Mining and Materials Engineering, McGill University, Montréal, Canada
Edward Luke
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York
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Cited
15 citations as recorded by crossref.
- Using artificial neural networks to predict riming from Doppler cloud radar observations T. Vogl et al. 10.5194/amt-15-365-2022
- Two Layers of Melting Ice Particles Within a Single Radar Bright Band: Interpretation and Implications H. Li & D. Moisseev 10.1029/2020GL087499
- Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production H. Li et al. 10.5194/acp-21-14671-2021
- Distinct secondary ice production processes observed in radar Doppler spectra: insights from a case study A. Billault-Roux et al. 10.5194/acp-23-10207-2023
- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
- Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106 C. Barrientos-Velasco et al. 10.5194/acp-22-9313-2022
- Identification of Concurrent Clear-Air and Precipitation Doppler Profiles for VHF Radar and an Incorporating Study of Strongly Convective Precipitation with Dual-Polarized Microwave Radiometer S. Tsai et al. 10.3390/atmos13040557
- Оцінювання параметрів відбиттів від метеоутворень по енергетичному спектру їх суміші з відбиттями від місцевих предметів Д. Атаманський et al. 10.30748/soi.2022.169.01
- Optimized analysis for sensitive detection and analysis of single proteins via interferometric scattering microscopy H. Dastjerdi et al. 10.1088/1361-6463/ac2f68
- PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations T. Vogl et al. 10.5194/amt-17-6547-2024
- peakTree: a framework for structure-preserving radar Doppler spectra analysis M. Radenz et al. 10.5194/amt-12-4813-2019
- Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar A. Teisseire et al. 10.5194/amt-17-999-2024
- A Novel Overlapping ME Peaks Decomposition Algorithm Based on Iterative Derivative Sharpening W. He et al. 10.1109/TIM.2024.3374307
- Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks W. Schimmel et al. 10.5194/amt-15-5343-2022
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. 10.3390/rs15215234
15 citations as recorded by crossref.
- Using artificial neural networks to predict riming from Doppler cloud radar observations T. Vogl et al. 10.5194/amt-15-365-2022
- Two Layers of Melting Ice Particles Within a Single Radar Bright Band: Interpretation and Implications H. Li & D. Moisseev 10.1029/2020GL087499
- Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production H. Li et al. 10.5194/acp-21-14671-2021
- Distinct secondary ice production processes observed in radar Doppler spectra: insights from a case study A. Billault-Roux et al. 10.5194/acp-23-10207-2023
- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
- Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106 C. Barrientos-Velasco et al. 10.5194/acp-22-9313-2022
- Identification of Concurrent Clear-Air and Precipitation Doppler Profiles for VHF Radar and an Incorporating Study of Strongly Convective Precipitation with Dual-Polarized Microwave Radiometer S. Tsai et al. 10.3390/atmos13040557
- Оцінювання параметрів відбиттів від метеоутворень по енергетичному спектру їх суміші з відбиттями від місцевих предметів Д. Атаманський et al. 10.30748/soi.2022.169.01
- Optimized analysis for sensitive detection and analysis of single proteins via interferometric scattering microscopy H. Dastjerdi et al. 10.1088/1361-6463/ac2f68
- PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations T. Vogl et al. 10.5194/amt-17-6547-2024
- peakTree: a framework for structure-preserving radar Doppler spectra analysis M. Radenz et al. 10.5194/amt-12-4813-2019
- Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar A. Teisseire et al. 10.5194/amt-17-999-2024
- A Novel Overlapping ME Peaks Decomposition Algorithm Based on Iterative Derivative Sharpening W. He et al. 10.1109/TIM.2024.3374307
- Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks W. Schimmel et al. 10.5194/amt-15-5343-2022
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. 10.3390/rs15215234
Latest update: 23 Nov 2024
Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
In a cloud, different particles like liquid water droplets and ice particles can exist...
