A quality control procedure for long-term series of daily precipitation data in a semiarid environment

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Abstract

The availability of quality precipitation records in the current climate situation is of great importance in the scientific-technical field but also for the public institutions that manage the meteorological networks. This work has implemented a comprehensive spatial quality control procedure in the semiarid region of Andalusia (Southern Spain), using precipitation time series from 1947 stations from three meteorological networks: Spanish Meteorological Agency (AEMET), Agroclimatic Information Network of Andalusia (RIA), and Phytosanitary Information Alert Network (RAIF). The method consists of three consecutive steps: basic, absolute, and relative quality control processes. The latter step compares data from neighboring stations taking into account their proximity, height difference, and correlation, leading to a complete evaluation of each daily value. Finally, the quality of each year at each station can be declared as acceptable, good, or excellent. The automatic weather station networks RIA and RAIF gave absolute quality index Q above 85% for almost 87% of their stations, while only 57% of AEMET network reached this percentage. However, one of the longest AEMET datasets, San Fernando-Cádiz, obtained, except for 1 year, Q values over 90% in all available years for more than a century of measurements, since 1870 until 2000. From a total of more than 15 million daily records, almost 82% was flagged as correct. Despite the limitations of Andalusia region (low density of stations and its structural water deficit), the complete quality control procedure has been satisfactorily applied. Finally, related to the number of outliers, no temporal trend was found across the region. Graphical abstract: [Figure not available: see fulltext.].

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APA

Estévez, J., Llabrés-Brustenga, A., Casas-Castillo, M. C., García-Marín, A. P., Kirchner, R., & Rodríguez-Solà, R. (2022). A quality control procedure for long-term series of daily precipitation data in a semiarid environment. Theoretical and Applied Climatology, 149(3–4), 1029–1041. https://doi.org/10.1007/s00704-022-04089-2

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