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

• Published in: Atmospheric measurement techniques Vol. 13; no. 7; pp. 3835 - 3853
• Main Authors: Polz, Julius, Chwala, Christian, Graf, Maximilian, Kunstmann, Harald
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 15-07-2020; Copernicus Publications
• Subjects: Analysis; Artificial neural networks; Atmospheric precipitations; Attenuation; Bias; Cable television broadcasting industry; Classification; Data; Data analysis; Data processing; Datasets; Detection; Developing countries; Feature extraction; LDCs; Levels; Meteorological radar; Meteorological radar stations; Meteorological services; Methods; Microwave attenuation; Microwave communications; Neural networks; Neurons; Pattern recognition; Precipitation estimation; Radar; Radar rainfall; Rain; Rain gauges; Rainfall; Robustness; Rolling (ship motion); Signal processing; Time series; Training; Visual cortex; Weather; Weather forecasting; Weather radar; United States; Netherlands; Germany
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-13-3835-2020

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. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. 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 1.4×105 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 CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 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 state-of-the-art 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 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. 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 all of Germany.