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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Tropospheric delays are one of the main contributors to the interferometric phase in synthetic aperture radar (SAR) interferometry. When the phase contributions from surface deformation, topography, and ionospheric delays are negligible or known, the interferogram can be used to estimate the differential tropospheric delay (DTD), which can help to improve tropospheric delay predictions from weather models and in situ measurements. In conventional repeat-pass interferometric SAR (InSAR), however, the estimation of the DTD can still be significantly hindered by baseline errors. In addition, a single interferogram provides only relative DTDs, as the delays can be retrieved up to an unknown offset. To address such issues, this article presents a method for the estimation of DTDs on large scales by using repeat-pass simultaneous multi-angle SAR systems. Complementary simultaneous observations of the correlated troposphere from multiple angles are used to retrieve estimates of the absolute DTD and, at the same time, to mitigate the effect of baseline knowledge errors. Finally, a performance evaluation is presented for the Harmony Earth Explorer 10 candidate mission. A centimeter-level absolute accuracy and a submillimeter-level relative accuracy of the DTD estimation are achieved under the multistatic Harmony case when at least one companion satellite has an inter-satellite distance longer than 300 km to provide enough sensitivity.

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This article describes the observation techniques and suggests processing methods to estimate dynamical sea-ice parameters from data of the Earth Explorer 10 candidate Harmony. The two Harmony satellites will fly in a reconfigurable formation with Sentinel-1D. Both will be equipped with a multi-angle thermal infrared sensor and a passive radar receiver, which receives the reflected Sentinel-1D signals using two antennas. During the lifetime of the mission, two different formations will be flown. In the stereo formation, the Harmony satellites will fly approximately 300km in front and behind Sentinel-1, which allows for the estimation of instantaneous sea-ice drift vectors. We demonstrate that the addition of instantaneous sea-ice drift estimates on top of the daily integrated values from feature tracking have benefits in terms of interpretation, sampling and resolution. The wide-swath instantaneous drift observations of Harmony also help to put high-temporal-resolution instantaneous buoy observations into a spatial context. Additionally, it allows for the extraction of deformation parameters, such as shear and divergence. As a result, Harmony's data will help to improve sea-ice statistics and parametrizations to constrain sea-ice models. In the cross-track interferometry (XTI) mode, Harmony's satellites will fly in close formation with an XTI baseline to be able to estimate surface elevations. This will allow for improved estimates of sea-ice volume and also enables the retrieval of full, two-dimensional swell-wave spectra in sea-ice-covered regions without any gaps. In stereo formation, the line-of-sight diversity allows the inference of swell properties in both directions using traditional velocity bunching approaches. In XTI mode, Harmony's phase differences are only sensitive to the ground-range direction swell. To fully recover two-dimensional swell-wave spectra, a synergy between XTI height spectra and intensity spectra is required. If selected, the Harmony mission will be launched in 2028.

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The Rhine-Meuse-Scheldt delta is shaped by natural and manmade landscapes. Over many polder areas, soils are drained to be used as pastures. Around 30% of the pastures are situated on peat soils, of which many are located in the western part of the Netherlands, known as the ‘Green Heart’. Peat is composed of organic materials that oxidize and emit greenhouse gases when exposed to air as a consequence of the draining. Oxidation of peat soils results in volume reduction and subsequent subsidence. As a result, the groundwater level rises relative to the surface. Consequently, the soil needs to be dewatered to keep it sufficiently dry for farming, resulting in more oxidation, and therefore more subsidence. This process is bound to continue until the peat soils have disappeared completely. The societal cost of land subsidence due to peat soils are estimated to be 5200 million euro for urban areas and 200 million euro for peatland pastures, for a period until 2050.@en

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