506 citations · 560 across the 6 of their papers we have counts for
6 papers
Advancing Subseasonal Forecasting with Machine Learning
Hannah Guan, Soukayna Mouatadid, Paulo Orenstein +10
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented a…
Adaptive Bias Correction for Improved Subseasonal Forecasting
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler +4
Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mit…
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler +8
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting com…
Online Learning with Optimism and Delay
Genevieve Flaspohler, Francesco Orabona, Judah Cohen +4
Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guar…
WeatherBench: A benchmark dataset for data-driven weather forecasting
Stephan Rasp, Peter D. Dueben, Sebastian Scher +3
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could al…
Recovering the parameters underlying the Lorenz-96 chaotic dynamics
Soukayna Mouatadid, Pierre Gentine, Wei Yu +1
Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to res…