PDEClimate-Net: A Partial Differential Equation-Driven Adaptive Framework for High-Performance Weather Forecasting and Climate Change Modeling

Abstract

Weather and climate prediction and modelling are critical to preparedness for natural hazards, environmental sustainability and management of resources. The conventional Partial Differential Equation (PDE)-based numerical weather prediction models are able to simulate weather by maintaining the physical consistency of the simulation but suffer from the disadvantage of high computational complexity and limited real time performance. In this paper, we are present a new framework, PDEclimate-Net, which consists of a combination of physics-based atmospheric modelling, adaptive deep learning and high-performance computing (HPC), to enhance the precision and efficiency of climate forecasting. The proposed approach includes adaptive PDE solving techniques, physics-based feature optimization and parallel computing to simulate complex spatiotemporal climate phenomena while maintaining physical consistency. Experimental results on the benchmark weather datasets show that the proposed method PDEClimate-Net can achieve an accuracy of 98.64%, a precision of 98.29%, a recall of 98.47%, an F1-score of 98.38% and an AUC of 99.12%, and reduce the computational times by 34.8% compared with existing state of the art methods. The results show that the effectivity, scalability and robustness of the PDEClimate-Net approach to next generation weather forecasting and climate change modeling are well demonstrated.