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Detection of Hail Storms in Radar Imagery Using Deep Learning
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Author and Affiliation:
Pullman, Melinda(Alabama Univ., Huntsville, AL, United States)
Gurung, Iksha(Alabama Univ., Huntsville, AL, United States)
Ramachandran, Rahul(NASA Marshall Space Flight Center, Huntsville, AL, United States)
Maskey, Manil(NASA Marshall Space Flight Center, Huntsville, AL, United States)
Abstract: In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.
Publication Date: Dec 11, 2017
Document ID:
20170012190
(Acquired Dec 19, 2017)
Subject Category: METEOROLOGY AND CLIMATOLOGY
Report/Patent Number: MSFC-E-DAA-TN49902
Document Type: Oral/Visual Presentation
Meeting Information: AGU Fall Meeting 2017; 11-15 Dec. 2017; New Orleans, LA; United States
Meeting Sponsor: American Geophysical Union; Washington, DC, United States
Contract/Grant/Task Num: NNM11AA01A
Financial Sponsor: NASA Marshall Space Flight Center; Huntsville, AL, United States
Organization Source: NASA Marshall Space Flight Center; Huntsville, AL, United States
Description: 1p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright; Public use permitted
NASA Terms: HAIL; HAILSTORMS; DETECTION; METEOROLOGICAL RADAR; RADAR IMAGERY; NEURAL NETS; PREDICTION ANALYSIS TECHNIQUES; MACHINE LEARNING; DATA PROCESSING; COMPUTER VISION; STORMS (METEOROLOGY); UNITED STATES
Other Descriptors: HAIL; PREDICTION; NEURAL NETWORK; NATURAL HAZARD
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