Selecting the five best ENSO ensemble members by their past errors and correlations, then scoring them on the same past data, shows large in-sample gains over the all-member mean but provides no out-of-sample evidence for real forecast skill.
Combined dynamical –deep learning ENSO forecasts,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.ao-ph 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon
Selecting the five best ENSO ensemble members by their past errors and correlations, then scoring them on the same past data, shows large in-sample gains over the all-member mean but provides no out-of-sample evidence for real forecast skill.