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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Bibliografische Detailangaben

Hauptverfasser:	Melika Jahangiri, Mahdi Asghari, Mohammad Hossein Niksokhan, Mohammad Reza Nikoo
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2025-07-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Precipitation downscaling BiLSTM neural networks Adaptive Kalman filtering Extreme event forecasting Climate change scenarios Deep learning
Online-Zugang:	https://doi.org/10.1038/s41598-025-08264-z
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