SCENT: A new precipitation nowcasting method based on sparse correspondence and deep neural network

Wei Fang, Feihong Zhang, Victor S. Sheng, Yewen Ding

Research output: Contribution to journalArticlepeer-review

Abstract

Precipitation nowcasting is an important research topic in meteorology, which relates to many aspects of people's life and social development. Under the combined influence of resolution and corresponding timestamp, a certain nonlinear relationship is satisfied between the echo intensity and precipitation. Therefore, the short-term precipitation prediction scheme based on radar echo extrapolation has become the main research method. By analyzing the spatiotemporal characteristics of the radar echo images, we found that precipitation results are related not only to currently observed radar echo images but also to some non-image features such as wind speed and shape of cloud clusters. Inspired by optical flow method, combined with the characteristics of radar reflectivity, we propose a new method SCENT to achieve precipitation prediction. Firstly, the sparse correspondence method based on Fast feature detection and SIFT matching is used to radar echo extrapolate and complete the extraction of non-image influence features. Afterwards, an improved neural network is utilized for regression calculation to obtain the total precipitation. By comparing with existing prediction models based on deep neural network, our new method can make precipitation nowcasting more accurate.

Original languageEnglish
Pages (from-to)10-20
Number of pages11
JournalNeurocomputing
Volume448
DOIs
StatePublished - Aug 11 2021

Keywords

  • Echo extrapolation
  • Neural network
  • Precipitation nowcasting
  • Sparse correspondence

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