Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images
CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09050 Pula, Italy
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Forecasting 2020, 2(2), 194-210; https://doi.org/10.3390/forecast2020011
Received: 26 May 2020
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Revised: 13 June 2020
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Accepted: 22 June 2020
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Published: 24 June 2020
(This article belongs to the Special Issue Feature Papers of Forecasting)
In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.
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Keywords:
nowcast; meteorological radar data; optical flow; deep learning
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MDPI and ACS Style
Marrocu, M.; Massidda, L. Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images. Forecasting 2020, 2, 194-210. https://doi.org/10.3390/forecast2020011
AMA Style
Marrocu M, Massidda L. Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images. Forecasting. 2020; 2(2):194-210. https://doi.org/10.3390/forecast2020011
Chicago/Turabian StyleMarrocu, Marino, and Luca Massidda. 2020. "Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images" Forecasting 2, no. 2: 194-210. https://doi.org/10.3390/forecast2020011
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