Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland
Abstract
:1. Introduction
2. Methodology
2.1. Instrument and Data Description
2.2. Machine Learning Algorithms
- a.
- Noise Discrimination Model
- b.
- Classification Model
2.3. Model Performance Evaluation
3. Results
4. Discussion and Suggestions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Model | Windcube 200S |
---|---|
manufacturer | Leosphere Inc. |
wavelength (μm) | 1.54 |
detection range (m) | 50–14,000 |
range resolution (m) | 25, 50, 75, 100 |
elevation angle (°) | −10–90 |
azimuth angle (°) | 0–360 |
Group | Class Name | Labeling Method * | Physical Explanation |
---|---|---|---|
1 | Low clouds | Highest backscatter coefficient; low depolarization ratio | Clouds within or close to the boundary layer; mostly water clouds in the training data set |
2 | High clouds | CNR threshold; DBSCAN clustering | Clouds in the free troposphere and at higher altitudes; separated from other non-noise data; mostly ice clouds, but could be water clouds |
3 | Rain | Descending movement; low depolarization ratio | Rain |
4 | Aerosol type I | High backscatter coefficient; high depolarization ratio ** | Dust particles in dry atmosphere; relative large size, high concentration with a non-spherical shape |
5 | Aerosol type II | Medium-high backscatter coefficient; high depolarization ratio | Dust particles in dry atmosphere; relative small size, low concentration, with a non-spherical shape |
6 | Aerosol type III | High backscatter coefficient; medium to high depolarization ratio | Dust particles in a humid atmosphere; relative high concentration, the shape is more spherical |
7 | Others | Unclassified data points | Other data points except for noise data |
8 | Noise | CNR threshold; DBSCAN clustering | Noise signals |
Location | Date | CNR of Noise Points (dB) | ||
---|---|---|---|---|
Mean | Minimum | Maximum | ||
Reykjavik * | 06/15/2019 | −34.11 | −41.29 | −10.75 |
Reykjavik | 07/10/2019 | −33.88 | −41.28 | −12.82 |
Reykjavik * | 07/31/2019 | −32.40 | −40.25 | −10.75 |
Keflavik | 07/31/2019 | −33.92 | −49.79 | −12.82 |
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Yang, S.; Peng, F.; von Löwis, S.; Petersen, G.N.; Finger, D.C. Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sens. 2021, 13, 2433. https://doi.org/10.3390/rs13132433
Yang S, Peng F, von Löwis S, Petersen GN, Finger DC. Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sensing. 2021; 13(13):2433. https://doi.org/10.3390/rs13132433
Chicago/Turabian StyleYang, Shu, Fengchao Peng, Sibylle von Löwis, Guðrún Nína Petersen, and David Christian Finger. 2021. "Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland" Remote Sensing 13, no. 13: 2433. https://doi.org/10.3390/rs13132433
APA StyleYang, S., Peng, F., von Löwis, S., Petersen, G. N., & Finger, D. C. (2021). Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sensing, 13(13), 2433. https://doi.org/10.3390/rs13132433