most citedUsing Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts

2 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2024★ 1 cited

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…

physics.ao-ph2024★ 2 cited

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…