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

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 trop...

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Autores principales:	Fangjia Dou, Xiaolei Lv, Huiming Chai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	interferometric synthetic aperture radar atmospheric correction numerical weather prediction model ensemble forecasting stochastic physic perturbation tendency weather research forecasting Science Q
Acceso en línea:	https://doaj.org/article/85716598f3c048b89b5c81e9f7b3462a
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spelling	oai:doaj.org-article:85716598f3c048b89b5c81e9f7b3462a2021-11-25T18:55:16ZMitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model10.3390/rs132246702072-4292https://doaj.org/article/85716598f3c048b89b5c81e9f7b3462a2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4670https://doaj.org/toc/2072-4292The 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.Fangjia DouXiaolei LvHuiming ChaiMDPI AGarticleinterferometric synthetic aperture radaratmospheric correctionnumerical weather prediction modelensemble forecastingstochastic physic perturbation tendencyweather research forecastingScienceQENRemote Sensing, Vol 13, Iss 4670, p 4670 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	interferometric synthetic aperture radar atmospheric correction numerical weather prediction model ensemble forecasting stochastic physic perturbation tendency weather research forecasting Science Q
spellingShingle	interferometric synthetic aperture radar atmospheric correction numerical weather prediction model ensemble forecasting stochastic physic perturbation tendency weather research forecasting Science Q Fangjia Dou Xiaolei Lv Huiming Chai Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
description	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.
format	article
author	Fangjia Dou Xiaolei Lv Huiming Chai
author_facet	Fangjia Dou Xiaolei Lv Huiming Chai
author_sort	Fangjia Dou
title	Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
title_short	Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
title_full	Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
title_fullStr	Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
title_full_unstemmed	Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model
title_sort	mitigating atmospheric effects in insar stacking based on ensemble forecasting with a numerical weather prediction model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/85716598f3c048b89b5c81e9f7b3462a
work_keys_str_mv	AT fangjiadou mitigatingatmosphericeffectsininsarstackingbasedonensembleforecastingwithanumericalweatherpredictionmodel AT xiaoleilv mitigatingatmosphericeffectsininsarstackingbasedonensembleforecastingwithanumericalweatherpredictionmodel AT huimingchai mitigatingatmosphericeffectsininsarstackingbasedonensembleforecastingwithanumericalweatherpredictionmodel
_version_	1718410536556691456

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

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