Improving seasonal forecast using probabilistic deep learning

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[Submitted on 27 Oct 2020]

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Abstract: The path toward realizing the potential of seasonal forecasting and its
socioeconomic benefits depends heavily on improving general circulation model
based dynamical forecasting systems. To improve dynamical seasonal forecast, it
is crucial to set up forecast benchmarks, and clarify forecast limitations
posed by model initialization errors, formulation deficiencies, and internal
climate variability. With huge cost in generating large forecast ensembles, and
limited observations for forecast verification, the seasonal forecast
benchmarking and diagnosing task proves challenging. In this study, we develop
a probabilistic deep neural network model, drawing on a wealth of existing
climate simulations to enhance seasonal forecast capability and forecast
diagnosis. By leveraging complex physical relationships encoded in climate
simulations, our probabilistic forecast model demonstrates favorable
deterministic and probabilistic skill compared to state-of-the-art dynamical
forecast systems in quasi-global seasonal forecast of precipitation and
near-surface temperature. We apply this probabilistic forecast methodology to
quantify the impacts of initialization errors and model formulation
deficiencies in a dynamical seasonal forecasting system. We introduce the
saliency analysis approach to efficiently identify the key predictors that
influence seasonal variability. Furthermore, by explicitly modeling uncertainty
using variational Bayes, we give a more definitive answer to how the El
Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates
global seasonal predictability.

Submission history

From: Baoxiang Pan [view email]


[v1]
Tue, 27 Oct 2020 21:02:26 UTC (47,107 KB)

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