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An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models

Wang, Youning; Yang, Zhiyun; Liu, Qi; Liu, Xiaodong

Authors

Youning Wang

Zhiyun Yang

Qi Liu



Abstract

Short-term heavy rainfall can have a significant impact on people's production, life and travel. Numerical Weather Prediction (NWP) is complex. It can predict weather conditions for the next week or even two weeks, but cannot predict the weather in the near hours in a timely manner. The TREC and Optical Flow methods used in the meteorological field are only good at translational transformations, and it is difficult to predict the generation and dissipation of cloud systems. ConvLSTM, PredRNN, and other deep RNN-based methods input only one map at each time step, and do not make good use of the spatial information of several neighboring moments. Inspired by schedule sampling, a new sampling scheme is proposed to allow the model to effectively use the spatial information of the past few times. Also, the advantages of MSE and MAE are combined to change the loss function.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date Jul 25, 2022
Online Publication Date Dec 13, 2022
Publication Date 2022
Deposit Date Jan 27, 2023
Publisher Institute of Electrical and Electronics Engineers
Book Title 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
DOI https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927983