Refined Reconstruction of Geophysical Fields: Integrating Elevation-Informed Diffusion Processes with Enhanced Feature Spaces

High-precision data are crucial for accurately modeling and predicting complex atmospheric phenomena such as cloud formation and storm development. However, existing deep learning approaches often struggle to reproduce the rich details inherent in these phenomena, particularly when it comes to capturing the nuanced effects of elevation on atmospheric dynamics. Moreover, the efficient utilization of elevation information in enhancing model prediction remains a challenge, limiting the fidelity of reconstructed environmental fields. To address these issues, we introduce a framework that integrates Conditional Variational Autoencoders (CVAEs) with diffusion models, specifically designed to incorporate elevation data for the detailed reconstruction of geophysical fields. Our approach leverages the strength of CVAEs in generating a multi-level, feature-rich representation conditioned on elevation data, which serves as input to a diffusion process. The diffusion model, based on a U-Net architecture augmented with an additional decoder, refines these features, enabling the precise reconstruction of complex meteorological phenomena. Experimental results demonstrate a significant improvement in the reconstruction of 850 hPa-geopotential height fields, achieving a 13.89% reduction in root mean squared error (RMSE) compared to Super-Resolution Convolutional Neural Network (SRCNN).