Probabilistic Forecasting of El Niño Using Neural Network Models

[1]  A. J. Clarke,et al.  On the Warm Water Volume and Its Changing Relationship with ENSO , 2014 .

[2]  Alex J. Cannon,et al.  Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes , 2018, Stochastic Environmental Research and Risk Assessment.

[3]  A. Timmermann,et al.  El Niño–Southern Oscillation complexity , 2018, Nature.

[4]  Petra Friederichs,et al.  Decomposition and graphical portrayal of the quantile score , 2014 .

[5]  A. J. Clarke.  El Niño physics and El Niño predictability. , 2013, Annual review of marine science.

[6]  Matthieu Lengaigne,et al.  Influence of the state of the Indian Ocean Dipole on the following year’s El Niño , 2010 .

[7]  Jürgen Kurths,et al.  Disentangling different types of El Niño episodes by evolving climate network analysis. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Mojib Latif,et al.  Dynamics of Interdecadal Variability in Coupled Ocean–Atmosphere Models , 1998 .

[9]  Emilio Hernández-García,et al.  Percolation-based precursors of transitions in extended systems , 2016, Scientific Reports.

[10]  William W. Hsieh,et al.  Neural network forecasts of the tropical Pacific sea surface temperatures , 2006, Neural Networks.

[11]  Thomas M. Smith,et al.  Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons , 2017 .

[12]  James W. Taylor.  A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns , 2000 .

[13]  M. Iredell,et al.  The NCEP Climate Forecast System Version 2 , 2014 .

[14]  Alex J. Cannon.  Quantile regression neural networks: Implementation in R and application to precipitation downscaling , 2011, Comput. Geosci..

[15]  Dejian Yang,et al.  Progress in ENSO prediction and predictability study , 2018, National Science Review.

[16]  William W. Hsieh,et al.  Forecasting regional sea surface temperatures in the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors , 1998 .

[17]  M. Hoerling,et al.  ENSO variability, teleconnections and climate change , 2001 .

[18]  David L. T. Anderson,et al.  Decadal and Seasonal Dependence of ENSO Prediction Skill , 1995 .

[19]  Michael K. Tippett,et al.  Deterministic skill of ENSO predictions from the North American Multimodel Ensemble , 2017, Climate Dynamics.

[20]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[21]  Max J. Suarez,et al.  A Delayed Action Oscillator for ENSO , 1988 .

[22]  Fei-Fei Jin,et al.  An Equatorial Ocean Recharge Paradigm for ENSO. Part I: Conceptual Model , 1997 .

[23]  A. Barnston,et al.  Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing? , 2012 .

[24]  P. Friederichs,et al.  Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression , 2007 .

[25]  William W. Hsieh,et al.  Forecasting the equatorial Pacific sea surface temperatures by neural network models , 1997 .

[26]  Dake Chen,et al.  El Niño prediction and predictability , 2008, J. Comput. Phys..

[27]  A. Barnston,et al.  Toward an Improved Multimodel ENSO Prediction , 2015 .

[28]  Shlomo Havlin,et al.  Very early warning of next El Niño , 2014, Proceedings of the National Academy of Sciences.

[29]  M. Balmaseda,et al.  Evaluation of the ECMWF ocean reanalysis system ORAS4 , 2013 .

[30]  S. Hameed,et al.  A model for super El Niños , 2018, Nature Communications.

[31]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[32]  Christopher K. Wikle,et al.  Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data , 2017, Entropy.

[33]  Jeong-Hwan Kim,et al.  Deep learning for multi-year ENSO forecasts , 2019, Nature.

[34]  R. Koenker,et al.  Goodness of Fit and Related Inference Processes for Quantile Regression , 1999 .

[35]  Henk A. Dijkstra,et al.  Using network theory and machine learning to predict El Niño , 2018, Earth System Dynamics.

[36]  Coherent Tropical Indo-Pacific Interannual Climate Variability , 2016 .