2023
DOI: 10.1029/2023wr035088
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Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes

Noelia Otero,
Pascal Horton

Abstract: In recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in th… Show more

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Cited by 2 publications
(3 citation statements)
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“…M. Hamill et al, 2015;Ben Daoud et al, 2016), climate change studies (e.g., Dayon et al, 2015;Raynaud et al, 2016), or past climate reconstruction (Caillouet et al, 2016). AMs are also used for other predictands, such as precipitation radar images (Foresti et al, 2015;Panziera et al, 2011), temperature (Caillouet et al, 2016;Delle Monache et al, 2013;Jézéquel et al, 2017;Raynaud et al, 2016), wind (Delle Monache et al, 2013;Delle Monache et al, 2011;Vanvyve et al, 2015; Alessandrini, Delle Monache, more and more popular in the context of forecasting, postprocessing and downscaling (e.g., Chapman et al, 2022;Leinonen et al, 2020;Miralles et al, 2022;Otero & Horton, 2023), AMs are still relevant and offer the benefit of interpretability. Some AMs combine different predictors together using weights in the calculation of the distances between the target and the analog situations (e.g., Keller et al, 2017;Meech et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…M. Hamill et al, 2015;Ben Daoud et al, 2016), climate change studies (e.g., Dayon et al, 2015;Raynaud et al, 2016), or past climate reconstruction (Caillouet et al, 2016). AMs are also used for other predictands, such as precipitation radar images (Foresti et al, 2015;Panziera et al, 2011), temperature (Caillouet et al, 2016;Delle Monache et al, 2013;Jézéquel et al, 2017;Raynaud et al, 2016), wind (Delle Monache et al, 2013;Delle Monache et al, 2011;Vanvyve et al, 2015; Alessandrini, Delle Monache, more and more popular in the context of forecasting, postprocessing and downscaling (e.g., Chapman et al, 2022;Leinonen et al, 2020;Miralles et al, 2022;Otero & Horton, 2023), AMs are still relevant and offer the benefit of interpretability. Some AMs combine different predictors together using weights in the calculation of the distances between the target and the analog situations (e.g., Keller et al, 2017;Meech et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…AMs are also used for other predictands, such as precipitation radar images (Foresti et al., 2015; Panziera et al., 2011), temperature (Caillouet et al., 2016; Delle Monache et al., 2013; Jézéquel et al., 2017; Raynaud et al., 2016), wind (Delle Monache et al., 2013; Delle Monache et al., 2011; Vanvyve et al., 2015; Alessandrini, Delle Monache, Sperati, & Nissen, 2015; Junk, Delle Monache, Alessandrini, Cervone, & von Bremen, 2015; Junk, Delle Monache, & Alessandrini, 2015), and solar radiation or power production (Alessandrini, Delle Monache, Sperati, & Cervone, 2015; Bessa et al., 2015; Raynaud et al., 2016). Although deep learning methods nowadays become more and more popular in the context of forecasting, postprocessing and downscaling (e.g., Chapman et al., 2022; Leinonen et al., 2020; Miralles et al., 2022; Otero & Horton, 2023), AMs are still relevant and offer the benefit of interpretability.…”
Section: Introductionmentioning
confidence: 99%
“…Although deep learning methods nowadays become more and more popular in the context of forecasting, postprocessing and downscaling (e.g. Chapman et al, 2022;Leinonen et al, 2020;Miralles et al, 2022;Otero & Horton, 2023), AMs are still relevant and offer the benefit of interpretability. Some AMs combine different predictors together using weights in the calculation of the distances between the target and the analog situations (e.g.…”
Section: Introductionmentioning
confidence: 99%