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Climate of the Past An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/cp-2017-64
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
16 May 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Climate of the Past (CP).
Effects of undetected data quality issues on climatological analyses
Stefan Hunziker1,2, Stefan Brönnimann1,2, Juan Marcos Calle3, Isabel Moreno3, Marcos Andrade3, Laura Ticona3, Adrian Huerta4, and Waldo Lavado-Casimiro4 1Institute of Geography, University of Bern, Switzerland
2Oeschger Centre for Climate Change Research, University of Bern, Switzerland
3Laboratorio de Física de la Atmósfera, Instituto de Investigaciones Físicas, Universidad Mayor de San Andrés, La Paz, Bolivia
4Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI), Lima, Peru
Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature, and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control method strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets, and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.

Citation: Hunziker, S., Brönnimann, S., Calle, J. M., Moreno, I., Andrade, M., Ticona, L., Huerta, A., and Lavado-Casimiro, W.: Effects of undetected data quality issues on climatological analyses, Clim. Past Discuss., https://doi.org/10.5194/cp-2017-64, in review, 2017.
Stefan Hunziker et al.
Stefan Hunziker et al.
Stefan Hunziker et al.

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Many data quality problems occurring in manned weather station observations are hardly detected with common data quality control methods. We investigated the effects of undetected data quality issues, and found that they may reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional trends. Applying adequate quality control approaches is of utmost importance.
Many data quality problems occurring in manned weather station observations are hardly detected...
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