CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension

• URL: http://arxiv.org/abs/2506.19340v3
• Date: Tue, 01 Jul 2025 22:43:36 GMT
• Title: CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension
• Authors: Jiahui Hu, Wenjun Dong,
• Abstract summary: We present CAM-NET, an AI model designed to predict neutral atmospheric variables from the Earth's surface to the ionosphere.<n>The model effectively predicts key atmospheric parameters, including zonal and meridional winds, temperature, and time rate of pressure.
• Score: 0.5256237513030104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Compressible Atmospheric Model-Network (CAM-NET), an AI model designed to predict neutral atmospheric variables from the Earth's surface to the ionosphere with high accuracy and computational efficiency. Accurate modeling of the entire atmosphere is critical for understanding the upward propagation of gravity waves, which influence upper-atmospheric dynamics and coupling across atmospheric layers. CAM-NET leverages the Spherical Fourier Neural Operator (SFNO) to capture global-scale atmospheric dynamics while preserving the Earth's spherical structure. Trained on a decade of datasets from the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (WACCM-X), CAM-NET demonstrates accuracy comparable to WACCM-X while achieving a speedup of over 1000x in inference time, can provide one year simulation within a few minutes once trained. The model effectively predicts key atmospheric parameters, including zonal and meridional winds, temperature, and time rate of pressure. Inspired by traditional modeling approaches that use external couplers to simulate tracer transport, CAM-NET introduces a modular architecture that explicitly separates tracer prediction from core dynamics. The core backbone of CAM-NET focuses on forecasting primary physical variables (e.g., temperature, wind velocity), while tracer variables are predicted through a lightweight, fine-tuned model. This design allows for efficient adaptation to specific tracer scenarios with minimal computational cost, avoiding the need to retrain the entire model. We have validated this approach on the $O^2$ tracer, demonstrating strong performance and generalization capabilities.

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