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2024 | OriginalPaper | Buchkapitel

Evaluation of Near Surface Climatology in Atmospheric Delay Estimation for Application in SAR Interferometry

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Abstract

Microwave signal propagation caused by spatiotemporal differences of dry and wet atmospheric content results in an effect known as atmospheric delay. Atmospheric delay is a main source to obscure the land deformation signal derived from Interferometry. A very sparsely distributed GPS network is continually measuring atmospheric delay. However, as climatology parameters show strong spatiotemporal variability, the GPS derived delays are not interpolatable. Instead, InSAR community utilizes climatologic profiles provided by models or reanalysis. This process is time consuming and suffers from the lack of accuracy in terms of spatial and temporal resolutions. In this study, based on a statistical method, we try to link the total delay and near surface climatology. Using four years of hourly climatological profiles from ERA-5 reanalysis (2017–2020), we investigated the vertical autocorrelation within climatological parameter and dry and wet delay profiles within 25 barometric pressure levels (until 13.5 km). The analysis shows that, at about 3.5 km altitude, the accumulated Q, and accordingly, wet delay reaches maximum and remains unchanged upward. However, T and dry delay continue to decrease and increase respectively. We evaluate the results using GNSS GPS total zenith path delay (ZPD) for three stations in the region of study. GPS total zenith path delay showed a mean RMS value of 28 mm and 23 mm with bulk and near surface climatology respectively.

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Literatur
Zurück zum Zitat Cheng, S., Perissin, D., Lin, H., & Chen, F. (2012). Atmospheric delay analysis from GPS meteorology and InSARA PS. Cheng, S., Perissin, D., Lin, H., & Chen, F. (2012). Atmospheric delay analysis from GPS meteorology and InSARA PS.
Zurück zum Zitat Foster, J., Brooks, B., Cherubini, T., Shacat, C., Businger, S., & Werner, C. (2006). Mitigating atmospheric noise for InSAR using a high resolution weather model. Geophysical Research Letters, 33. Foster, J., Brooks, B., Cherubini, T., Shacat, C., Businger, S., & Werner, C. (2006). Mitigating atmospheric noise for InSAR using a high resolution weather model. Geophysical Research Letters, 33.
Zurück zum Zitat Hanssen, R. F. (2001). Radar interferometry: Data interpretation and error analysis, (Vol. 2). Springer.CrossRef Hanssen, R. F. (2001). Radar interferometry: Data interpretation and error analysis, (Vol. 2). Springer.CrossRef
Zurück zum Zitat Hu, Z., & Mallorqui, J. J. (2019). An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model. Department of Signal Theory and Communications (TSC), Universitat Politècnica de Catalunya (UPC). Hu, Z., & Mallorqui, J. J. (2019). An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model. Department of Signal Theory and Communications (TSC), Universitat Politècnica de Catalunya (UPC).
Zurück zum Zitat Krishnakumar, V., Monserrat O., Crosetto, M., & Crippa, B. (2018). Atmospheric phase delay in sentinel SAR interferometry. Krishnakumar, V., Monserrat O., Crosetto, M., & Crippa, B. (2018). Atmospheric phase delay in sentinel SAR interferometry.
Zurück zum Zitat Yu, C., & Li, Z. (2020). Towards a new generation of Generic Atmospheric Correction Online Service for InSAR-GACOS 2.0 Yu, C., & Li, Z. (2020). Towards a new generation of Generic Atmospheric Correction Online Service for InSAR-GACOS 2.0
Metadaten
Titel
Evaluation of Near Surface Climatology in Atmospheric Delay Estimation for Application in SAR Interferometry
verfasst von
Ayoub Moradi
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-43922-3_107