Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Abstract

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

“…Due to different atmospheric conditions in the repeat-pass interferometric mode, the propagation path and propagation speed of SAR ranging signals will change in the atmosphere, introducing atmospheric delay errors in InSAR-DEM [20,24]. Therefore, the core factors affecting DEM accuracy are systematic errors and atmospheric delay errors [24,25].…”

“…Moreover, it also interferes with the height matching between DEMs and GCPs, as well as between TPs, thus affecting the calibration of systematic errors. However, the existing atmospheric effects correction methods usually rely on external water vapor data [19] or require extensive SAR data to perform spatiotemporal analyses [20], limiting the real-time correction of atmospheric effects on a global scale.…”

Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model‐Derived Wet Delay Corrections

“…After assimilating GPS PWV map combined with Meteosat satellite data, the corresponding RMSE values were reduced to around 1.8 kg/m 2 . In recent years, many studies have adopted WRF model to correct atmospheric delay in geodetic surveying, such as InSAR surveying (Dou et al, 2021;Murray et al, 2019;Wang et al, 2021;Webb et al, 2020). However, very little work has studied WRF-derived atmospheric delay to augment GNSS PPP (Gong et al, 2021;Vaclavovic et al, 2017;Wilgan et al, 2017).…”

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes

“…Therefore, researchers have attempted to correct the tropospheric delay in InSAR using external auxiliary data, which estimated the zenith total delay (ZTD) in the line-of-sight (LOS) direction for each SAR data acquisition moment using external auxiliary data. The commonly used external data sources fall into several categories: meteorological reanalysis data represented by ERA5 (European Center for Medium-Range Weather Forecasts Reanalysis v5) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; numerical weather forecast models represented by WRF (weather research and forecasting model) [ 37 , 38 , 39 , 40 ]; precipitable water vapor (PWV) products represented using MERIS (medium-resolution imaging spectrometer), Sentinel-3 OLCI (ocean and land color instrument); and MODIS (moderate-resolution imaging spectroradiometer) [ 41 , 42 , 43 , 44 ]. These data types originate from different sensors, and exhibit spatial continuity, clearly representing the lateral heterogeneity of tropospheric water vapor content.…”