1 citations · 1 across the 2 of their papers we have counts for
3 papers
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
Jakob Schloer, Steffen Tietsche, Christopher D. Roberts +6
Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors ac…
Ensemble reliability and the signal-to-noise paradox in ECMWF subseasonal forecasts
Christopher David Roberts, Frederic Vitart
Ensemble forecasts can exhibit counterintuitive statistical properties such that the correlation between ensemble means and observations () exceeds the correlation between…
Regularization of ML models for Earth systems by using longer model timesteps
Raghul Parthipan, Mohit Anand, Hannah M Christensen +3
Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inpu…