4.7 Article

Multi-Year ENSO Forecasts Using Parallel Convolutional Neural Networks With Heterogeneous Architecture

Related references

Note: Only part of the references are listed.
Article Geochemistry & Geophysics

Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks

Mayuna Gupta et al.

Summary: Recent studies suggest that data-driven machine learning models can overcome the spring predictability barrier in predicting El Nino Southern Oscillation (ENSO) accurately. By using a convolutional long short-term memory (ConvLSTM) network, the monthly mean Nino3.4 index can be skillfully predicted up to one year ahead, including strong El Nino cases.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Multidisciplinary Sciences

Temporal Convolutional Networks for the Advance Prediction of ENSO

Jining Yan et al.

SCIENTIFIC REPORTS (2020)

Article Multidisciplinary Sciences

Purely satellite data-driven deep learning forecast of complicated tropical instability waves

Gang Zheng et al.

SCIENCE ADVANCES (2020)

Article Multidisciplinary Sciences

Deep learning for multi-year ENSO forecasts

Yoo-Geun Ham et al.

NATURE (2019)

Article Robotics

El Nino-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks

Clifford Broni-Bediako et al.

ARTIFICIAL LIFE AND ROBOTICS (2019)

Review Multidisciplinary Sciences

El Nino-Southern Oscillation and its impact in the changing climate

Song Yang et al.

NATIONAL SCIENCE REVIEW (2018)

Article Multidisciplinary Sciences

Predicting El Nino Beyond 1-year Lead: Effect of the Western Hemisphere Warm Pool

Jae-Heung Park et al.

SCIENTIFIC REPORTS (2018)

Article Computer Science, Theory & Methods

A scalable parallel algorithm for atmospheric general circulation models on a multi-core cluster

Yuzhu Wang et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2017)

Article Geochemistry & Geophysics

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Qin Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2017)

Review Geochemistry & Geophysics

The Defining Characteristics of ENSO Extremes and the Strong 2015/2016 El Nino

Agus Santoso et al.

REVIEWS OF GEOPHYSICS (2017)

Article Statistics & Probability

An ensemble quadratic echo state network for non-linear spatio-temporal forecasting

Patrick L. McDermott et al.

Article Environmental Sciences

Quantifying the impacts of ENSO and IOD on rain gauge and remotely sensed precipitation products over Australia

E. Forootan et al.

REMOTE SENSING OF ENVIRONMENT (2016)

Article Computer Science, Interdisciplinary Applications

Dynamic seasonality in time series

Mike K. P. So et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2014)

Article Meteorology & Atmospheric Sciences

The 'spring predictability barrier' for ENSO predictions and its possible mechanism: results from a fully coupled model

Wansuo Duan et al.

INTERNATIONAL JOURNAL OF CLIMATOLOGY (2013)

Article Meteorology & Atmospheric Sciences

AN OVERVIEW OF CMIP5 AND THE EXPERIMENT DESIGN

Karl E. Taylor et al.

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY (2012)

Article Meteorology & Atmospheric Sciences

Prediction of Indian summer monsoon rainfall using Nino indices: A neural network approach

Ravi P. Shukla et al.

ATMOSPHERIC RESEARCH (2011)

Article Oceanography

El Nino variability in simple ocean data assimilation (SODA), 1871-2008

Benjamin S. Giese et al.

JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS (2011)

Article Economics

Direct multi-step estimation and forecasting

Guillaume Chevillon

JOURNAL OF ECONOMIC SURVEYS (2007)

Article Mathematics, Interdisciplinary Applications

Receptive field atlas and related CNN models

V Gál et al.

INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS (2004)