# [PERSON] Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
## 1 Introduction  
Weather radar, which uses electromagnetic waves to identify precipitations in the atmosphere, is one of the frequently used remote sensing devices. It can provide observation results with remarkably high spatiotemporal resolution in a wide observation area, which is beneficial to meteorological research and services. There are a large number of applications utilizing the observation results, such as quantitative precipitation estimation [1], convective storm nowcasting [2], downburst prediction [3], and post-event analysis [4]. Unfortunately, undesirable echoes, which are also called non-meteorological echoes, might contaminate the observation results when weather radar determines the location and the intensity of the precipitation. Sea clutter [5], ground clutter [6], and anomalous propagation [7] are typical examples of the non-meteorological echoes and make a quality control process [8] complicated by their similar characteristics to meteorological echoes. Therefore, a large number of domestic and foreign scholars have paid attention to implementing precise prediction methods or automated quality control processes to alleviate the experts' tasks based on statistical [9] and machine learning methods, such as fuzzy logic [10], Bayesian [11], random forest [12], and clustering [13]. Furthermore, lots of meteorological researchers and forecasters have recently focused on deep learning-based approaches, which have shown their superior capability to solve real-world problems in various fields [14]. An adequate amount of data and equally distributed data are positively necessary to implement an effective and appropriately working machine learning method. However, it is nearly impossible to obtain ideal datasets when solving real-world problems: most of them tend to be imbalanced and insufficient. In the downburst prediction using machine learning techniques [15], for example, it requires enormous exertion to gather a sufficient amount of information on each specific class, eventhough weather radar data are for free and plentiful in many countries. Data augmentation [16] is a popular selection method for solving the problems mentioned above by utilizing the given dataset. It can increase the quantity of the entire dataset to make it sufficient. By utilizing the data augmentation, it allows saving time and resources by generating realistic data based on a relatively small number of observed cases annotated by experts. Additionally, it can adjust the balance between majority and minority classes by increasing or decreasing in quantity of the minor or major class data. There are several traditional approaches to obtain augmented data: cropping; brightness control; and simple geometric transformations such as translation, rotation, and resizing. While traditional approaches can relieve some of those problems, they fundamentally generate data highly correlated with the data provided. In this case, GAN (generative adversarial network) [17] can be the best alternative solution. The GAN consists of two models, the generator and the discriminator. They intensely compete and cooperate while training--the generator endeavors to produce elaborate counterfeit data that thoroughly fools the discriminator, and the discriminator exerts itself to distinguish between the counterfeit data and the original data. By using this intriguing structure, it has been providing a novel form of data augmentation and proving its potential in various fields, including natural language processing [18], hyperspectral image classification [19], and medical imaging [20]. Consequently, the active studies of the GAN yield a massive number of fascinating methods. However, which model is appropriate to solve a problem in a given situation remains a concern. In this paper, we propose exploring the problem of data augmentation for radar data analysis and evaluate different models with actual radar observation results.