BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios

BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios

Abstract Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-...

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Bibliographic Details
Main Authors: Melika Jahangiri, Mahdi Asghari, Mohammad Hossein Niksokhan, Mohammad Reza Nikoo
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Precipitation downscaling
BiLSTM neural networks
Adaptive Kalman filtering
Extreme event forecasting
Climate change scenarios
Deep learning
Online Access:https://doi.org/10.1038/s41598-025-08264-z
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