A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM

  • Ekasit PhermphoonphiphatEmail author
  • Tomohiko Tomita
  • Masayuki Numao
  • Ken-ichi Fukui
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 12)


Spatiotemporal prediction on climate data is aiming to predict future spatial data by learning from prior spatial sequence data. In this paper, we are interested in a prediction of upper tropospheric circulations over the Northern Hemisphere by predicting a geopotential height at 300 hPa (Z300) variable. We proposed a predictive model by constructing an architecture with convolutional layers and deconvolutional layers and applied to convolutional long short-term memory (ConvLSTM) network. The results show that our model obtained root mean square error (RMSE) of 77.36 m (0.84% comparing to average Z300 value) in short-term prediction. While, a convolutional neural network (CNN) and a linear regression (LR) model obtained RMSE of 109.35 (1.19%) and 153.61 (1.67%), respectively. The ConvLSTM maintains RMSE even in long-term prediction. Furthermore, the prediction features’ investigation result shows that temperature at 300 hPa (T300) and self prior Z300 features are important for Z300 prediction.


Upper tropospheric circulations Spatial correlation ConvLSTM 



This work was supported in part by the Network Joint Research Center for Materials and Devices and by JSPS KAKENHI Grant Number 19K22876.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ekasit Phermphoonphiphat
    • 1
    Email author
  • Tomohiko Tomita
    • 2
  • Masayuki Numao
    • 3
  • Ken-ichi Fukui
    • 3
  1. 1.Graduate School of Information Science and TechnologyOsaka UniversitySuitaJapan
  2. 2.Faculty of Advanced Science and TechnologyKumamoto UniversityKumamotoJapan
  3. 3.The Institute of Scientific and Industrial ResearchOsaka UniversitySuitaJapan

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