Forecasting of tropospheric delay using AI foundation models in support of microwave remote sensing

Abstract

Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.