Applying Deep Learning to Hail Detection: A Case Study

Deep learning is a subset of machine learning that uses deep neural networks (DNNs) capable of learning representations and extracting valuable information from vast data sets. Similarly, weather phenomena are often identified by patterns in data that serve as precursor signatures. Therefore, deep learning networks can be used to identify signatures of the weather phenomena, or possibly signatures not yet established by forecasters in addition to aiding forecasters in synthesizing the growing amount of meteorological observations. In this article, we demonstrate the value of deep learning for atmospheric science applications by providing a proof of concept, using deep learning for the detection of hail-bearing storms as a test case study. The deep learning network presented in this article obtains a higher precision when presented with multisource data and is able to identify a common feature associated with hail storms-decreased infrared brightness temperatures. This network and case study illustrate the capability of deep networks for the detection of weather phenomena and contribute to the growing awareness of deep learning among atmospheric scientists.

I. Introduction

A digital era has dawned; one in which data are now the world’s most valuable resource and serve as the engine for artificial intelligence techniques that allow computers to mimic the human learning process and extract valuable information from vast amounts of data. Technology giants like Amazon and Google increase their revenues by using artificial intelligence techniques to improve their services and products. In fact, these companies and other technology titans have used information obtained through artificial intelligence to improve their digital advertising, and as a result, have amassed a net profit of over U.S $25 billion in the first quarter of 2017 [1]. In recent years, artificial intelligence techniques have evolved to incorporate machine learning and deep learning networks. Machine learning utilizes statistical methods that enable machines or computers to learn when iteratively presented with more data or experiences. On the other hand, deep learning is a subset of machine learning that uses neural networks capable of learning representations or patterns within data sets to add value to large amounts of data [2].