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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 6, issue 1
Adv. Sci. Res., 6, 141–146, 2011
https://doi.org/10.5194/asr-6-141-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 6, 141–146, 2011
https://doi.org/10.5194/asr-6-141-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 May 2011

20 May 2011

Data validation procedures in agricultural meteorology – a prerequisite for their use

J. Estévez1, P. Gavilán2, and A. P. García-Marín1 J. Estévez et al.
  • 1University of Córdoba, Projects Engineering, Córdoba, Spain
  • 2IFAPA Center "Alameda del Obispo", Junta de Andalucía, Córdoba, Spain

Abstract. Quality meteorological data sources are critical to scientists, engineers, climate assessments and to make climate related decisions. Accurate quantification of reference evapotranspiration (ET0) in irrigated agriculture is crucial for optimizing crop production, planning and managing irrigation, and for using water resources efficiently. Validation of data insures that the information needed is been properly generated, identifies incorrect values and detects problems that require immediate maintenance attention. The Agroclimatic Information Network of Andalusia at present provides daily estimations of ET0 using meteorological information collected by nearly of one hundred automatic weather stations. It is currently used for technicians and farmers to generate irrigation schedules. Data validation is essential in this context and then, diverse quality control procedures have been applied for each station. Daily average of several meteorological variables were analysed (air temperature, relative humidity and rainfall). The main objective of this study was to develop a quality control system for daily meteorological data which could be applied on any platform and using open source code. Each procedure will either accept the datum as being true or reject the datum and label it as an outlier. The number of outliers for each variable is related to a dynamic range used on each test. Finally, geographical distribution of the outliers was analysed. The study underscores the fact that it is necessary to use different ranges for each station, variable and test to keep the rate of error uniform across the region.

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