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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-412
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-412
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 Dec 2019

Submitted as: research article | 19 Dec 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Julius Polz1, Christian Chwala1,2, Maximilian Graf1, and Harald Kunstmann1,2 Julius Polz et al.
  • 1Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
  • 2University of Augsburg, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany

Abstract. Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4 x 105 trainable parameters. With a structure, inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time-series. We test the CNNs ability to generalize to CMLs and time periods outside the training data. Our CNN is trained on four months of data from 400 randomly selected CMLs and validated on two different months of data, once for all CMLs and once for the 3504 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set we use the gauge adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 87 % of all rainy and 91 % of all non-rainy periods. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall induced attenuation patterns in CML signal levels from a large CML data set covering entire Germany.

Julius Polz et al.
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Julius Polz et al.
Model code and software

CNNs for rain event detection with CMLs J. Polz https://doi.org/10.5281/zenodo.3580275

Julius Polz et al.
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Short summary
Commercial microwave link (CML) networks can be used to estimate path averaged rain rates. This study evaluates the ability of convolutional neural networks to distinguish between wet and dry periods in CML time-series data and the ability to transfer this detection skill to sensors not used for training. Our data set consists of several months of data from 3904 CMLs covering entire Germany. Compared to a previously used detection method, we could show a significant increase in performance.
Commercial microwave link (CML) networks can be used to estimate path averaged rain rates. This...
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