Estimating Single-Epoch Integrated Atmospheric Refractivity From InSAR for Assimilation in Numerical Weather Models

Numerical weather prediction (NWP) models are used to predict the weather based on current observations in combination with physical and mathematical models. Yet, they are limited by the spatial density and the accuracy of the available observations. Satellite radar interferometry (InSAR) is known to be extremely sensitive to the 3-D atmospheric refractivity distribution and has a high spatial resolution, providing information that can be used for assimilation in NWP models. However, due to the inherent superposition of two or more atmospheric states, only biased and temporally differenced signals can be retrieved, which can also be contaminated by deformation signals and decorrelation. Here, we present a method to estimate single-epoch absolute atmospheric delays by combining InSAR time series with prior NWP model prediction time series, using a constrained least-squares estimation. We show that this leads to a solution that reliably extracts the single-epoch relative delays from InSAR data and uses prior NWP model data to find the absolute reference for these delays while mitigating long-term deformation and decorrelation signal. This approach leads to repetitive delay updates with a spatial resolution of 500 m, which can be directly assimilated into numerical weather models.

Automated summary

Numerical weather prediction models are limited by available observations, while satellite radar interferometry can provide high-resolution atmospheric information for assimilation. This study introduces a method to estimate atmospheric delays by combining InSAR data with NWP model data, mitigating the effects of deformation and decorrelation. The approach allows for repetitive updates with a spatial resolution of 500 m, suitable for assimilation into numerical weather models.