SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
arXiv (Cornell University)(2021)
摘要
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 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 variables 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 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.
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