Temporal Filtering of InSAR Data Using Statistical Parameters From NWP Models

Finding solutions for the mitigation of atmospheric phase delay patterns from differential synthetic aperture radar interferometry (d-InSAR) observations is currently one of the most active research topics in radar remote sensing. Recently, many studies have analyzed the performance of regional numerical weather prediction (NWP) models for this task; however, despite the significant efforts made to optimize model parameterizations, most of these studies have concluded that current regional NWPs are not able to robustly reproduce the atmospheric phase delay structures that affect SAR interferograms. Despite these previous findings, we have revisited the application of NWPs for atmospheric correction using a different analysis strategy. In contrast to earlier studies, which assessed the quality of NWP-derived phase screen data, we have studied NWPs from a statistical angle by analyzing whether they are able to provide realistic information about the statistical properties of atmospheric phase signals in d-InSAR data. We have determined that NWP forecasts can provide relevant statistical information about the atmospheric phase screen captured in d-InSAR data. Based on this, this study presents a new atmospheric phase filtering approach that is using statistical atmospheric information as a prior in order to optimize the choice of unknown filter parameters. The mathematical concept of the prior-driven filtering approach is outlined, and its implementation is explained. We have determined the performance of this new filter concept and have shown that it comes very close to a filter optimum.