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Daniel Medeiros Moreira

Researcher at Federal University of Rio de Janeiro

Publications -  25
Citations -  518

Daniel Medeiros Moreira is an academic researcher from Federal University of Rio de Janeiro. The author has contributed to research in topics: Altimeter & Environmental science. The author has an hindex of 11, co-authored 20 publications receiving 332 citations. Previous affiliations of Daniel Medeiros Moreira include University of Toulouse & Institut de recherche pour le développement.

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Stage-discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin

TL;DR: In this article, the authors demonstrate the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively, by applying satellite altimetry to a poorly gauged basin.
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Altimetry for the future: building on 25 years of progress

Saleh Abdalla, +360 more
TL;DR: In 2018, the 25th year of development of radar altimetry was celebrated and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences as discussed by the authors.
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Detection of Envisat RA2/ICE-1 retracked radar altimetry bias over the Amazon basin rivers using GPS

TL;DR: In this paper, the bias of the Envisat ICE-1 retracked altimetry over rivers is found to be 1.044 ± 0.212 m, which is a significant departure from other envisat calibrations.
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Absolute Calibration of Jason Radar Altimeters from GPS Kinematic Campaigns Over Lake Issykkul

TL;DR: In this paper, the authors presented results of calibration/validation of Jason-1 and Jason-2 satellite altimeters over Lake Issykkul located in Kyrgyzstan, which was chosen as a dedicated radar altimetry C/V site in 2004.
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Radar Altimetry Aids Managing Gauge Networks

TL;DR: In this article, the authors focus on examples from the Amazon basin where radar altimetry has been used to provide an independent dataset that can be used to support the management of hydrological observation networks by including new data together with conventional field data.