In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 26, No. 11 ( 2022-06-14), p. 2923-2938
Abstract:
Abstract. Precipitation forecasting is an important mission in weather
science. In recent years, data-driven precipitation forecasting techniques
could complement numerical prediction, such as precipitation nowcasting,
monthly precipitation projection and extreme precipitation event
identification. In data-driven precipitation forecasting, the predictive
uncertainty arises mainly from data and model uncertainties. Current deep
learning forecasting methods could model the parametric uncertainty by
random sampling from the parameters. However, the data uncertainty is
usually ignored in the forecasting process and the derivation of predictive
uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep
learning precipitation forecasting framework to estimate the predictive
uncertainty. Specifically, the data uncertainty is estimated a priori and
the input uncertainty is propagated forward through model weights according
to the law of error propagation. The model uncertainty is considered by
sampling from the parameters and is coupled with input and target data
uncertainties in the objective function during the training process.
Finally, the predictive uncertainty is produced by propagating the input
uncertainty in the testing process. The experimental results indicate that
the proposed joint uncertainty modeling framework for precipitation
forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-26-2923-2022
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2100610-6
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