Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China
In: Environmental Research Letters, Jg. 19 (2024), Heft 7, S. 074013-74013
Online
academicJournal
Zugriff:
Subseasonal precipitation forecasting is challenging but critical for water management, energy supply, and disaster prevention. To improve regional subseasonal precipitation prediction, previous studies have proposed a dynamical-statistical projection model (DSPM). In this study, we develop a new method that combines the DSPM and deep learning (DL), called the DL-DSPM. The DSPM is developed using the observed relationships between large-scale atmospheric circulations and regional precipitation, and the dynamical forecasted atmospheric circulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The DL-DSPM improves upon the DSPM by correcting biases in atmospheric circulation forecasts from the ECMWF model using two DL models, namely, residual network and U-Net models. In the case of Southwest China (SWC), DL models can improve atmospheric circulation forecasts at lead times beyond 5 pentads, including large-scale drivers of SWC precipitation variability. The DL-DSPM outperforms the ECMWF model and DSPM forecasts in predicting precipitation anomalies beyond 4 and 5 pentads over most SWC regions, respectively. In addition, the DL-DSPM is more skillful than the ECMWF model and DSPM in predicting extreme precipitation events more than 4 pentads in advance. The successful combination of DL and the DSPM provides a new possible direction for DL applications in subseasonal precipitation forecasting.
Titel: |
Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China
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Autor/in / Beteiligte Person: | Nie, Yanbo ; Sun, Jianqi |
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Zeitschrift: | Environmental Research Letters, Jg. 19 (2024), Heft 7, S. 074013-74013 |
Veröffentlichung: | IOP Publishing, 2024 |
Medientyp: | academicJournal |
ISSN: | 1748-9326 (print) |
DOI: | 10.1088/1748-9326/ad5370 |
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