GNSS wet-delay fluctuation parameters Bresil

This deposit accompanies the Brief Communication "GNSS water-vapour fluctuations read the air mass of the 2024 Rio Grande do Sul and 2022 Recife floods hours ahead of the heaviest rain" (Natural Hazards and Earth System Sciences, submitted).

Antennas of global navigation satellite systems estimate, as a by-product of positioning, the delay that water vapour imposes on satellite signals. Beyond the amount of vapour, the way this delay fluctuates between the minute and the hour carries information on how the moisture field is arranged. Two parameters summarise those fluctuations over each hour: an amplitude, sigma, and a decorrelation rate, lambda.

The deposit contains these two parameters, computed in sliding 62.5-minute windows for eleven antennas of the Brazilian Network for Continuous Monitoring of GNSS (RBMC), over 1 March to 15 May 2024 for six antennas of Rio Grande do Sul and 1 April to 10 June 2022 for five antennas of the coast of Northeast Brazil; the 30-second zenith wet delays from which they were derived; the six-hourly rain-gauge records of the NOAA Integrated Surface Database used as truth, with their decoding; the analysis code that leads from the parameters to every table, figure and number of the paper; the registers of predictions, each dated before the corresponding computation and kept as written, including those the data refuted; and the outputs reported in the paper.

The estimator that derives lambda and sigma from the wet delay is described in the cited references and is not part of this deposit. Reproduction of the published results starts from the parameters provided here.

Data and Resources

Cite this as

Gael Kermarrec (2026). GNSS wet-delay fluctuation parameters Bresil [Data set]. LUIS. https://doi.org/10.25835/v40ouls8
Retrieved: 01:34 13 Sep 2026 (UTC)

Additional Info

Field Value
Author Gael Kermarrec
Maintainer Gael Kermarrec
Last Updated September 12, 2026, 01:03 (UTC)
Created September 8, 2026, 12:46 (UTC)
License Creative Commons Attribution-NonCommercial 4.0 International
Dataset Size 40.1 MByte