AI-Based GNSS Data Assimilation of ERA5 3D Wet Refractivity Fields: Initial Results

Accurately representing atmospheric humidity is critical for Artificial Intelligence (AI)-based weather forecasting models, which mostly rely on physics-based weather data such as ERA5 in both training and deployment stages. Unlike physics-based weather forecasting models, which continuously use humidity observations from different sources through data assimilation techniques, AI weather models are not equipped with a data assimilation framework to integrate observations into their system. This is one of the most important limitations of these models, which limits their ability to forecast small-scale weather events that are mainly due to convection processes and are related to humidity. To address this gap, there is a need to develop an AI-based data assimilation framework for integrating reliable observations into current AI-based weather forecasting models as an auxiliary component.

Global Navigation Satellite Systems (GNSS) observations provide reliable humidity measurements with strong sensitivity to the wet refractivity of the atmosphere, which plays an important role in numerical weather prediction, GNSS positioning, and atmospheric monitoring.

In this study, as an initial step, we present a physics-informed deep learning-based framework to assimilate ground-based and space-based GNSS data into ERA5 Three-Dimensional (3D) wet refractivity fields.

The proposed framework assimilates ground-based GNSS Zenith Wet Delays (ZWD), GNSS Radio Occultation (RO) profiles, radiosonde measurements, and voxel mask data that represent the number of signal rays intersecting each voxel, as derived from a ray-tracing technique, to update an initial 3D wet refractivity field from ERA5 data. A 3D Convolutional Neural Network (3D-CNN), which uses residual and convolutional block attention modules, is employed to capture the nonlinear relationships between multi-source observations and 3D wet refractivity distributions. The assimilation procedure is formulated using a hybrid physics-informed loss function that simultaneously constrains (i) GNSS ZWD consistency at station locations, (ii) voxel-wise agreement with RO-derived wet refractivity, (iii) adherence to the ERA5-based initial state, and (iv) bias reduction in ZWD. The updated 3D wet refractivity field is evaluated using ZWD derived from independent GNSS observations and radiosonde measurements.

The obtained results demonstrate that the proposed deep learning-based assimilation framework significantly improves 3D wet refractivity estimation and ZWD accuracy relative to the initial ERA5-driven state, while producing physically consistent structures. The framework provides a scalable pathway for assimilating humidity data from different types of GNSS measurements and other remote sensing techniques into reanalysis datasets, thereby enhancing the meteorological parameters used in AI-based weather forecasting models.