F
Fadwa Alshawaf
Researcher at University of Potsdam
Publications - 15
Citations - 199
Fadwa Alshawaf is an academic researcher from University of Potsdam. The author has contributed to research in topics: GNSS applications & Interferometric synthetic aperture radar. The author has an hindex of 7, co-authored 14 publications receiving 115 citations.
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Journal ArticleDOI
Trends of Vertically Integrated Water Vapor over the Arctic during 1979–2016: Consistent Moistening All Over?
Annette Rinke,Benjamin Segger,Susanne Crewell,Marion Maturilli,Tuomas Naakka,Tiina Nygård,Timo Vihma,Fadwa Alshawaf,Galina Dick,Jens Wickert,Jens Wickert,Jan Keller +11 more
TL;DR: In this paper, the authors analyzed the Arctic trends of integrated water vapor based on four reanalyses and radiosonde data over 1979-2016 and found that the Arctic experiences a robust mo...
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Estimating trends in atmospheric water vapor and temperature time series over Germany
TL;DR: In this paper, the authors compare the temporal trends estimated from ground-based GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements.
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Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations
TL;DR: In this article, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated.
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On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series
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Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets
R. Van Malderen,E. Pottiaux,Anna Klos,Peter Domonkos,M. Elias,Tong Ning,Olivier Bock,José Antonio Guijarro,Fadwa Alshawaf,Mostafa Hoseini,A. Quarello,A. Quarello,Emilie Lebarbier,B. Chimani,V. Tornatore,S. Zengin Kazancı,Janusz Bogusz +16 more
TL;DR: The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous, and finds that most methods underestimate the number of breaks and have a significant number of false detections.