  Rainfall estimation through the analysis of its impact on electromagnetic
waves has sparked increasing interest in the research community. Recent studies
have delved into its effects on cellular network performance, demonstrating the
potential to forecast rainfall levels based on electromagnetic wave attenuation
during precipitations. This paper aims to solve the problem of identifying the
nature of specific weather phenomena from the received signal level (RSL) in
4G/LTE mobile terminals. Specifically, utilizing time-series data representing
RSL, we propose a novel approach to encode time series as images and model the
task as an image classification problem, which we finally address using
convolutional neural networks (CNNs). The main benefit of the abovementioned
procedure is the opportunity to utilize various data augmentation techniques
simultaneously. This encompasses applying traditional approaches, such as
moving averages, to the time series and enhancing the generated images. We have
investigated various image data augmentation methods to identify the most
effective combination for this scenario. In the upcoming sections, we will
introduce the task of rainfall estimation and conduct a comprehensive analysis
of the dataset used. Subsequently, we will formally propose a new approach for
converting time series into images. To conclude, the paper's final section will
present and discuss the experiments conducted, providing the reader with a
brief yet comprehensive overview of the results.
