August 20, 2025Open Access

Deep Learning-Based Predictive Modeling For Extreme Weather Events Under Climate Change

Puntos clave

Extreme weather events prediction accuracy improves significantly with deep learning, showcasing a 32% reduction in RMSE for heatwave intensity.
The novel ConvLSTM framework captures complex non-linear patterns, outperforming traditional weather prediction methods by 28% for hurricane trajectories.
Assessment utilized global datasets from 1980 to 2023, testing the model's performance under multiple climate scenarios.
Robust skill in projections for 2050 under RCP 8.5 suggests high potential for use in resilience planning and adaptation strategies.

Resumen

Increasing frequency and intensity of extreme weather events due to climate change necessitate accurate predictive models for mitigation and adaptation. This paper proposes a novel deep learning (DL) framework integrating Convolutional Long Short-Term Memory (ConvLSTM) networks and attention mechanisms for spatio-temporal prediction of extreme events (heatwaves, floods, hurricanes). Leveraging multi-source climate reanalysis data (ERA5, CMIP6 projections) and remote sensing imagery, the model captures complex non-linear patterns and teleconnections often missed by traditional Numerical Weather Prediction (NWP) and statistical methods. Evaluated on global datasets spanning 1980-2023, our approach reduces Root Mean Squared Error (RMSE) by 32% for heatwave intensity prediction and improves hurricane trajectory accuracy by 28% compared to ECMWF-IFS benchmarks. The model demonstrates robust skill in 2050 climate projections under RCP 8.5, highlighting its potential for climate resilience planning. Implementation challenges and scalability solutions are discussed.

Leer artículo completoexternamente

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo