Articles | Volume 9, issue 9
https://doi.org/10.5194/amt-9-4759-2016
https://doi.org/10.5194/amt-9-4759-2016
Research article
 | 
26 Sep 2016
Research article |  | 26 Sep 2016

A semiempirical error estimation technique for PWV derived from atmospheric radiosonde data

Julio A. Castro-Almazán, Gabriel Pérez-Jordán, and Casiana Muñoz-Tuñón

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Cited articles

Ahrens, C.: Meteorology Today: An Introduction to Weather, Climate, and the Environment, Brooks/Cole, 624 pp., 2003.
Barreto, A., Cuevas, E., Damiri, B., Guirado, C., Berkoff, T., Berjón, A. J., Hernández, Y., Almansa, F., and Gil, M.: A new method for nocturnal aerosol measurements with a lunar photometer prototype, Atmos. Meas. Tech., 6, 585–598, https://doi.org/10.5194/amt-6-585-2013, 2013.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System, J. Geophys. Res.-Atmos., 97, 15787–15801 , https://doi.org/10.1029/92JD01517, 1992.
Bevis, M., Businger, S., Chiswell, S., Herring, T. A., Anthes, R. A., Rocken, C., and Ware, R. H.: GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water, J. Appl. Meteorol., 33, 379–386, https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2, 1994.
Bird, R. E. and Hulstrom, R. L.: Precipitable Water Measurements with Sun Photometers, J. Appl. Meteorol., 21, 1196–1201, https://doi.org/10.1175/1520-0450(1982)021<1196:PWMWSP>2.0.CO;2, 1982.
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Short summary
Water vapour is the main responsible for the atmospheric extinction in astronomical observations in different bands. One of the most common and accurate techniques to measure it are the radiosoundings. A method to estimate the error and the optimum number of sampled levels is proposed, considering the uncertainties and the leakage in sampling, based on data from Roque de los Muchachos Observ. and Guimar (Canary Is., Spain), Lindenberg (Germany) and Ny-Ålesund (Norway). The median error is 2.0  %.