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Data Validation Procedures in Agricultural Meteorology – a Prerequisite for Their Use : Volume 6, Issue 1 (20/05/2011)

By Estévez, J.

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Book Id: WPLBN0003989000
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Data Validation Procedures in Agricultural Meteorology – a Prerequisite for Their Use : Volume 6, Issue 1 (20/05/2011)  
Author: Estévez, J.
Volume: Vol. 6, Issue 1
Language: English
Subject: Science, Advances, Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2011
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Description
Description: University of Córdoba, Projects Engineering, Córdoba, Spain. 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.

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

Excerpt
Allen, R. G.: Assessing integrity of weather data for reference evapotranspiration estimation, J. Irrig. Drain. Eng., 122(2), 97–106, 1996.; De Haro, J. M., Gavilán, P., and Fernández, R.: The Agroclimatic Information Network of Andalusia, Proceeding of the Third International Conference on Experiences with Automatic Weather Stations, Torremolinos, Spain, 19–21 February, 1–12, 2003.; Feng, S., Hu, Q., and Qian, Q.: Quality control of daily meteorological data in China, 1951-2000: a new dataset, Int. J. Climatol., 24, 853–870, 2004.; Gavilán, P., Lorite, I. J., Tornero, S., and Berengena, J.: Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment, Agric. Water Manag., 81, 257–281, 2006.; Gavilán, P., Estévez J., and Berengena, J.: Comparison of standardized reference evapotranspiration equations in southern Spain, J. Irrig. Drain. Eng. ASCE, 134(1), 1–12, 2008.; Hubbard, K. G., Goddard, S., Sorensen, W. D., Wells, N., and Osugi, T. T.: Performance of quality assurance procedures for an applied climate information system, J. Atmos. Oceanic Technol., 22, 105–112, 2005.; Meek, D. W. and Hatfield, J. L.: Data quality checking for single station meteorological databases, Agric. For. Meteor., 69, 85–109, 1994.; Meyer, S. J. and Hubbard, K. G.: Nonfederal automated weather stations and networks in the United States and Canada: a preliminary survey, B. Am. Meteorol. Soc., 73(4), 449–457, 1992.; O'Brien, K. J. and Keefer, T. N.: Real-time data verification, Proc. ASCE Special Conf., Buffalo, NY, American Society of Civil Engineers, 764–770, 1985.; PostGIS: http://postgis.refractions.net (last access: 5 December 2009), 2009.; PostgreSQL: http://www.postgresql.org (last access: 5 December 2009), 2009.; Shafer, M. A., Fiebrich, C. A., Arndt, D. S., Fredrickson, S. E., and Hughes, T. W.: Quality assurance procedures in the Oklahoma Mesonet, J. Atmos. Oceanic Technol., 17, 474–494, 2000.; Stonebraker, M. and Kemnitz, G.: The Postgres next-generation database-management system, Communicat. ACM., 34, 78–92, 1991.; Weiss, A. and Robb, J. G.: Results and interpretations from a survey on agriculturally related weather information, B. Am. Meteorol. Soc., 67(1), 10–15, 1986.

 

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