Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
arXiv (Cornell University)(2022)
摘要
Producing high-quality forecasts of key climate variables, such as
temperature and precipitation, on subseasonal time scales has long been a gap
in operational forecasting. This study explores an application of machine
learning (ML) models as post-processing tools for subseasonal forecasting.
Lagged numerical ensemble forecasts (i.e., an ensemble where the members have
different initial dates) and observational data, including relative humidity,
pressure at sea level, and geopotential height, are incorporated into various
ML methods to predict monthly average precipitation and two-meter temperature
two weeks in advance for the continental United States. Regression, quantile
regression, and tercile classification tasks using linear models, random
forests, convolutional neural networks, and stacked models (a multi-model
approach based on the prediction of the individual ML models) are considered.
Unlike previous ML approaches that often use ensemble mean alone, we leverage
information embedded in the ensemble forecasts to enhance prediction accuracy.
Additionally, we investigate extreme event predictions that are crucial for
planning and mitigation efforts. Considering ensemble members as a collection
of spatial forecasts, we explore different approaches to address spatial
variability. Trade-offs between different approaches may be mitigated with
model stacking. Our proposed models outperform standard baselines such as
climatological forecasts and ensemble means. This paper further includes an
investigation of feature importance, trade-offs between using the full ensemble
or only the ensemble mean, and different modes of accounting for spatial
variability.
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关键词
climate model ensembles,subseasonal forecasting,ensemble averages,climate model
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