Results 171 to 180 of about 347,574 (237)

Mapping the ionosphere with millions of phones. [PDF]

Nature
Smith J, Kast A, Geraschenko A, Morton YJ, Brenner MP, van Diggelen F, Williams BP.   +6 more
europepmc   +1 more source

A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval. [PDF]

Sci Rep
Sleem RE, Abdelfatah MA, Mousa AE, El-Fiky GS.   +3 more
europepmc   +1 more source

The Growth and Decay of Intense GNSS Amplitude and Phase Scintillation During Non-Storm Conditions. [PDF]

Space Weather
Madhanakumar M, Spicher A, Vierinen J, Oksavik K, Coster AJ, Huyghebaert DR, Martin CJ, Häggström I, Paxton LJ.   +8 more
europepmc   +1 more source

Optimized Station to Estimate Atmospheric Integrated Water Vapor Levels Using GNSS Signals and Meteorology Parameters

, 2016
B. Beldjilali, B. Benadda
semanticscholar   +1 more source

Plasma structuring within an expanded polar cap and cusp studied with the SS-520-3 sounding rocket. [PDF]

Earth Planets Space
Buschmann LM, Asamura K, Clausen LBN, Jin Y, Kojima H, Kumamoto A, Kurita S, Ogawa Y, Oksavik K, Saito Y, Spicher A, Yokota S, Miloch WJ.   +12 more
europepmc   +2 more sources

GPS Galileo BDS3 LEO uncalibrated phase delays estimation and tight combination precise point positioning with ambiguity resolution. [PDF]

Sci Rep
Fang J, Tu R, Zhang P, Zhang R, Cheng F, Lu X.   +5 more
europepmc   +1 more source

The 3-star project at Politecnico di Torino [PDF]

Corpino, Sabrina, Cucca, Manuela, Notarpietro, Riccardo, Ridolfi, Guido, Stesina, Fabrizio   +4 more
core  

Retrieval of refractivity fields from GNSS tropospheric delays: theoretical and data-based evaluation of collocation methods and comparisons with GNSS tomography. [PDF]

J Geod
Shehaj E, Geiger A, Rothacher M, Moeller G.   +3 more
europepmc   +1 more source

An observational study on the time and spatial variations of precipitable water vapor with a dense GNSS receiver network [PDF]

, 2014
Iwaki, Yuya
core  

Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning. [PDF]

Sensors (Basel)
Liu N, Shen Y, Zhang S, Zhu X.
europepmc   +1 more source