|Table of Contents|

MEPM: MultivariateENSOPredictionModel Based on Deep Learning(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2024年第03期
Page:
285-297
Research Field:
环境与可持续发展
Publishing date:

Info

Title:
MEPM: MultivariateENSOPredictionModel Based on Deep Learning
Author(s):
FANG Wei1; 2; 3; 4; 5*;  ZHANG Xiao-zhi1; 2;  QI Mei-han1; 2
(1. School of ComputerScience, Nanjing University of Information Science & Technology, Nanjing210044, Jiangsu, China; 2. Engineering Research Center of Digital Forensics of Ministry ofEducation, Nanjing University of Information Science & Technology, Nanjing 210044,Jiangsu,China; 3. Key Laboratory of Transportation Meteorology of ChinaMeteorologicalAdministration, NanjingJoint Institutefor AtmosphericSciences, Nanjing 210041, Jiangsu, China; 4. ChinaMeteorologicalAdministration Basin Heavy Rainfall Key Laboratory/Hubei KeyLaboratory forHeavy Rain Monitoringand Warning Research,Institute of HeavyRain,China Meteorological Administration,Wuhan 430205,Hubei,China; 5. Jiangsu Provincial KeyLaboratory for ComputerInformation Processing Technology,SoochowUniversity, Suzhou 215006, Jiangsu, China)
Keywords:
climate change;  ENSO;  multi-climate variables;  deep learning;  spatio-temporal sequence prediction;  convolutional neural network
PACS:
P467; TP18
DOI:
10.19814/j.jese.2023.08029
Abstract:
The El Niño-Southern Oscillation(ENSO)is an anomaly of air-sea interaction on an interannual time scale in the tropical Pacific Ocean, and its occurrence is characterized by the Niño3.4 index. In addition, ENSO is closely related to many extreme climatic events. Therefore, effective ENSO prediction is of great significance for preventing extreme climate events andin-depth study of global climate change. However, most ENSO predictions based on deep learning predict an index or a single variable, and there are few research on the space-time evolution of ENSO under the simulation of multi-climate factors.MEPM, a multivariate ENSO prediction model, was presented. These include multivariate information extraction module(MIEM)and spatial-temporal fusion module(STFM), to capture the interdependencies of different climate elements in time and space, thereby improving the accuracy of ENSO prediction. The anomalies oflatitudewind stress anomaly(τx),longitudewind stress anomaly(τy),sea surface temperature anomaly(SSTA)and 150 m below sea surface temperature anomaly(SSTA150)were selected, and sufficient experiments were carried out. The results show that MEPM is 10%, 20%and 14% higher, respectively, than dynamic forecasting systemsCanCM4, CCSM3, and GFDL-aer04 in North American multi-model ensemble on the average of Niño3.4 index-related techniques 11 months in advance. In addition, MEPM significantly outperforms other deep learning models on Niño3.4 index-related techniques over the medium term and provides valid predictions up to 17 months.

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Last Update: 2024-05-30