Performance analysis of atmospheric correction in InSAR data based on the Weather Research and Forecasting Model (WRF)

• Published in: 2010 IEEE International Geoscience and Remote Sensing Symposium pp. 2900 - 2903
• Main Authors: Gong, W, Meyer, F, Webley, P W, Morton, D, Liu, S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2010
• Subjects: Analytical models; atmospheric correction; Atmospheric measurements; Atmospheric modeling; Data models; Delay; InSAR; Meteorology; Numerical Weather Models (NWM); Predictive models; SAR
• ISBN: 1424495652; 9781424495658
• ISSN: 2153-6996; 2153-7003
• DOI: 10.1109/IGARSS.2010.5652267

The influence of the turbulent atmosphere is seen as the main performance limitation for high-quality Interferometric Synthetic Aperture Radar (InSAR) techniques in ground deformation monitoring applications. Atmospheric correction using numerical weather prediction (NWP) models is widely seen as a promising emerging technology for mitigation of atmospheric signals. First results showed promising capabilities for correction of stratified delay yet have revealed limited performance for modeling and mitigating turbulent atmospheric water vapor signals from SAR [1, 2]. This paper presents an integration of InSAR observations with predictions from the high-resolution Weather Research and Forecasting Model (WRF). Special focus is put on investigating improvements in the weather model parameterization to achieve enhanced performance in atmospheric correction. First, a statistical analysis of the quality of absolute delay predictions is presented based on a comparison of vertically integrated WRF delays with radiosonde measurements. Second, the performance of WRF for atmospheric correction of InSAR data is analyzed by comparing WRF phase delay maps to SAR interferograms and analyzing structure functions and variances of the residual atmospheric delay signal. Here, significant improvements could be achieved through modifications of the WRF model parameterization, which are highlighted in Section 3.2. From our study, we conclude that the performance of latest generation high-resolution NWPs can be significantly improved if the setup and parameterization of the model domain is optimized.