Advancing sub-seasonal to seasonal multi-model ensemble precipitation prediction in east asia: Deep learning-based post-processing for improved accuracy.

ABSTRACT: The growing interest in Subseasonal to Seasonal (S2S) prediction data across different industries underscores its potential use in comprehending weather patterns, extreme conditions, and important sectors such as agriculture and energy management. However, concerns about its accuracy have been raised. Furthermore, enhancing the precision of rainfall predictions remains challenging in S2S forecasts. This study enhanced the sub-seasonal to seasonal (S2S) prediction skills for precipitation amount and occurrence over the East Asian region by employing deep learning-based post-processing techniques. We utilized a modified U-Net architecture that wraps all its convolutional layers with TimeDistributed layers as a deep learning model. For the training datasets, the precipitation prediction data

SUBMITTER: Chung U 

PROVIDER: S-EPMC11385763 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature