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• W2982503897 abstract "In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts." @default.
• W2982503897 created "2019-11-08" @default.
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• W2982503897 date "2020-02-01" @default.
• W2982503897 modified "2023-10-17" @default.
• W2982503897 title "Mapping Precipitable Water Vapor Time Series From Sentinel-1 Interferometric SAR" @default.
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• W2982503897 doi "https://doi.org/10.1109/tgrs.2019.2946077" @default.
• W2982503897 hasPublicationYear "2020" @default.
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