2 citations · 3 across the 5 of their papers we have counts for
5 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…
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…
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…
Improving forecasts of precipitation extremes over Northern and Central Italy using machine learning
Federico Grazzini, Joshua Dorrington, Christian M. Grams +3
The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid prog…
Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall
Joshua Dorrington, Christian Grams, Federico Grazzini +2
A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events c…