TITLE: SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking 

ABSTRACT: Subseasonal forecasting of the weather two to six weeks in advance is
critical for resource allocation and climate adaptation but poses many
challenges for the forecasting community. At this forecast horizon,
physics-based dynamical models have limited skill, and the targets for
prediction depend in a complex manner on both local weather and global climate
variables. Recently, machine learning methods have shown promise in advancing
the state of the art but only at the cost of complex data curation, integrating
expert knowledge with aggregation across multiple relevant data sources, file
formats, and temporal and spatial resolutions. To streamline this process and
accelerate future development, we introduce SubseasonalClimateUSA, a curated
dataset for training and benchmarking subseasonal forecasting models in the
United States. We use this dataset to benchmark a diverse suite of subseasonal
models, including operational dynamical models, classical meteorological
baselines, and ten state-of-the-art machine learning and deep learning-based
methods from the literature. Overall, our benchmarks suggest simple and
effective ways to extend the accuracy of current operational models.
SubseasonalClimateUSA is regularly updated and accessible via the
https://github.com/microsoft/subseasonal_data/ Python package.
Subseasonal forecasting of the weather two to six weeks in advance is
critical for resource allocation and climate adaptation but poses many
challenges for the forecasting community. At this forecast horizon,
physics-based dynamical models have limited skill, and the targets for
prediction depend in a complex manner on both local weather and global climate
variables. Recently, machine learning methods have shown promise in advancing
the state of the art but only at the cost of complex data curation, integrating
expert knowledge with aggregation across multiple relevant data sources, file
formats, and temporal and spatial resolutions. To streamline this process and
accelerate future development, we introduce SubseasonalClimateUSA, a curated
dataset for training and benchmarking subseasonal forecasting models in the
United States. We use this dataset to benchmark a diverse suite of subseasonal
models, including operational dynamical models, classical meteorological
baselines, and ten state-of-the-art machine learning and deep learning-based
methods from the literature. Overall, our benchmarks suggest simple and
effective ways to extend the accuracy of current operational models.
SubseasonalClimateUSA is regularly updated and accessible via the
https://github.com/microsoft/subseasonal_data/ Python package.
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
http://arxiv.org/abs/2109.10399v3