2 citations · 6 across the 5 of their papers we have counts for
5 papers
Knowledge-guided machine learning for disentangling Pacific sea surface temperature variability across timescales
Kyle J. C. Hall, Maria J. Molina, Emily F. Wisinski +2
Global weather and climate patterns are strongly influenced by dominant modes of anomalous Pacific sea surface temperature (SST) variability, including the El Niño-Southern Oscilla…
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…
Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO Variance
Jakob Schlör, Felix Strnad, Antonietta Capotondi +1
El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical…