Interferometric Synthetic Aperture Radar (InSAR) has a wide range of applications, including the monitoring of solid-earth and cryospheric geophysical processes and the monitoring of the built environment. The use of InSAR for atmospheric applications is less thoroughly developed. To perform such analyses the atmospheric phase delay of the SAR signal between different overpasses is used, which needs to be disentangled from other phase constituents, such as displacements and topography, which requires stack processing of large data volumes. Typically, initial atmospheric delays are predicted using existing numerical weather prediction (NWP) models, but InSAR processing and NWP model delay estimation software are not well integrated. Here we present a pure Python-based software package that integrates the automatic downloading and processing of InSAR and NWP model data to create time-series of unwrapped InSAR interferograms and InSAR equivalent tropospheric delays from NWP models. By combining the geometry of the InSAR radar signals with different NWP model datasets the tropospheric delays can accurately be derived on a pixel by pixel basis.

Due to its sensitivity to water vapor, high resolution, and global availability, interferometric satellite radar (InSAR) has a large but unexploited potential for the improvement of regional NWP models. A relatively straightforward approach is to exploit the exact instantaneous character of the InSAR data in data assimilation to improve the timing of NWP model realizations. Here we show the potential impact of InSAR data on the NWP model timing and subsequently on improved model performance. By time-shifting the model to find the best match with the InSAR data we show that we can achieve a model error reduction (one-sigma) of up to 40% in cases where weather fronts are present, while other cases show more modest improvements. Most model performance gain due to time-shifts can therefore be achieved in cases where weather fronts are present over the study area. The model-timing errors related to the maximum model error reduction for these cases are in the order of (Formula presented.) 30 min.

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.

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.

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.