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Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations

1
Department of Ecology and Ecosystem Management, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, D-85354 Freising, Germany
2
Institute for Advanced Study, Technical University of Munich, Lichtenbergstraße 2a, D-85748 Garching, Germany
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(5), 251; https://doi.org/10.3390/atmos10050251
Received: 24 April 2019 / Revised: 3 May 2019 / Accepted: 4 May 2019 / Published: 7 May 2019
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Abstract

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity. View Full-Text
Keywords: convective; stratiform; rain microstructure; Thies; disdrometer; classification; machine learning convective; stratiform; rain microstructure; Thies; disdrometer; classification; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ghada, W.; Estrella, N.; Menzel, A. Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations. Atmosphere 2019, 10, 251.

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