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Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Author

Listed:
  • Marino Marrocu

    (CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09050 Pula, Italy)

  • Luca Massidda

    (CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09050 Pula, Italy)

Abstract

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 10 4 km 2 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.

Suggested Citation

  • Marino Marrocu & Luca Massidda, 2020. "Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images," Forecasting, MDPI, vol. 2(2), pages 1-17, June.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:11-210:d:375822
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    Citations

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    Cited by:

    1. Sonia Leva, 2021. "Editorial for Special Issue: “Feature Papers of Forecasting”," Forecasting, MDPI, vol. 3(1), pages 1-3, February.
    2. Marino Marrocu & Luca Massidda, 2022. "Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars," Forecasting, MDPI, vol. 4(4), pages 1-21, October.

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