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 3D 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, that 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, that can be directly assimilated into numerical weather models.

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The influence of signal delay due to the varying atmospheric refractivity can be significant in individual interferograms. This signal is generally considered to be noise in deformation studies, but it can also potentially be used to improve weather models [1]. This application has an enormous potential, because, contrary to deformation studies, every acquired SAR image contains valuable information on the state of the atmosphere.@en

InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weathermodels [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency andaccessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR)values in numerical weather models. Although studies exist on comparison between InSAR data and weathermodels [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. Inthis study we present different ways to assimilate DIR values in an operational weather model and show the firstforecast results.@en

Over the last decades, several case studies confirmed that it is possible to isolate the atmospheric signal delay from synthetic aperture radar (SAR) imagery and showed very promising results. However, the temporal or spatial coverage of the available satellite missions was low, which restricted the application to case studies only. With the launch of the Sentinel-1 satellites a new SAR dataset became available, with unprecedented temporal and spatial coverage. In this study we show that differential integrated refractivity (DIR) from SAR imagery can be a great data source for both validation of weather models validation and for data assimilation in weather forecasts. To derive the DIR estimates a stack of sentinel-1 images is processed and continuously updated. The general repeat time of the Sentinel 1a is 12 days at the equator, but by using a combination of overlapping descending and ascending orbits a repeat time of 3 to 4 can be achieved above mid-latitudes. This repeat time can be reduced to 1 to 2 days once the data from the Sentinel 1b becomes available. The DIR values are calculated by subtracting SAR retrievals at different epochs, which are corrected for topography and earth curvature. Then the resulting DIR maps or interferograms are unwrapped and the integrated refractivity of individual epochs is derived using a network approach. Finally, refractivity data from global navigation satellite system (GNSS) is used to convert relative refractivity values to absolute refractivity values. This method results in refractivity maps with much higher resolution and accuracy than current operational weather models and measurement networks. Therefore, it gives us a unique dataset to validate weather models or improve weather models using data assimilation. Additionally, these refractivity maps could give us more insight in weather phenomena, which are not captured by other measuring techniques. To demonstrate the quality and resolution of this dataset, we will show our results alongside maps of calculated refractivity based on weather model parameters. The development of these time series is a first step to the generation of an operational refractivity product and the implementation of this product into operational weather models.@en