2 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime
Jakob Schloer, Matthew Newman, Jannik Thuemmel +2
While deep-learning models have demonstrated skillful El Niño Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simul…
Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction
Zijie Guo, Pumeng Lyu, Fenghua Ling +8
Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despi…
Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
Kinya Toride, Matthew Newman, Andrew Hoell +3
We introduce an interpretable-by-design method, optimized model-analog, that integrates deep learning with model-analog forecasting which generates forecasts from similar initial c…