Applying Deep Learning to Hail Detection: A Case Study

Abstract

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.