Subseasonal Climate Forecasting
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Python library for Optimistic Online Learning under Delay (PoolD)
September 2021
This Python package implements algorithms for online learning under delay using optimistic hints. More details on the algorithms and their regret properties can be found in the manuscript Online Learning with Optimism and Delay.
Subseasonal Forecasting Toolkit
September 2021
The subseasonal_toolkit package provides implementations of the subseasonal forecasting toolkit models, machine learning models, and meteorological baselines presented in the preprint Learned Benchmarks for Subseasonal Forecasting Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight,…
Subseasonal Data Python Package
August 2021
The subseasonal_data package provides an API for loading and manipulating the SubseasonalClimateUSA dataset developed for training and benchmarking subseasonal forecasting models. Here, subseasonal refers to climate and weather forecasts made 2-6 weeks in advance. See DATA.md for a description of…
The SubseasonalRodeo Dataset
September 2018
A benchmark dataset for training and evaluating subseasonal forecasting systems—systems predicting temperature or precipitation 2-6 weeks in advance—in the western contiguous United States.