Identifying, attributing, and overcoming common data quality issues of manned station observations

Stefan Hunziker, Stefanie Gubler, Juan Calle, Isabel Moreno, Marcos Andrade, Fernando Velarde, Laura Ticona, Gualberto Carrasco, Yaruska Castellón, Clara Oria, Mischa Croci-Maspoli, Thomas Konzelmann, Mario Rohrer, Stefan Brönnimann

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

In situ climatological observations are essential for studies related to climate trends and extreme events. However, in many regions of the globe, observational records are affected by a large number of data quality issues. Assessing and controlling the quality of such datasets is an important, often overlooked aspect of climate research. Besides analysing the measurement data, metadata are important for a comprehensive data quality assessment. However, metadata are often missing, but may partly be reconstructed by suitable actions such as station inspections. This study identifies and attributes the most important common data quality issues in Bolivian and Peruvian temperature and precipitation datasets. The same or similar errors are found in many other predominantly manned station networks worldwide. A large fraction of these issues can be traced back to measurement errors by the observers. Therefore, the most effective way to prevent errors is to strengthen the training of observers and to establish a near real-time quality control (QC) procedure. Many common data quality issues are hardly detected by usual QC approaches. Data visualization, however, is an effective tool to identify and attribute those issues, and therefore enables data users to potentially correct errors and to decide which purposes are not affected by specific problems. The resulting increase in usable station records is particularly important in areas where station networks are sparse. In such networks, adequate selection and treatment of time series based on a comprehensive QC procedure may contribute to improving data homogeneity more than statistical data homogenization methods.

Original languageEnglish
Pages (from-to)4131-4145
Number of pages15
JournalInternational Journal of Climatology
Volume37
Issue number11
DOIs
StatePublished - Sep 2017

Bibliographical note

Funding Information:
This work is part of the project ‘Data on climate and Extreme weather for the Central AnDEs’ (DECADE), no. IZ01Z0_147320, which is financed by the Swiss Program for Research on Global Issues for Development (r4d), and the project ‘Servicios CLIMáticos con énfasis en los ANdes en apoyo a las DEcisioneS’ (CLIMANDES), no. 7F-08453.01, funded by the Swiss Agency for Development and Cooperation (SDC) and coordinated by the World Meteorological Organization (WMO). We further thank Jenny Chavez, Roberto Catacora, and Fernando Figeroa from AASANA for their great support, Andrew Rhines for the helpful conversation regarding the decoding of measurement precisions, and Paulo Vitor da Costa Pereira and Yuri Brugnara for the information and exchange about data quality issues in Brazilian and Italian networks, respectively. SB acknowledges support from the FP7 project ERA-CLIM2.

Publisher Copyright:
© 2017 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Keywords

  • Bolivia
  • Peru
  • data homogenization
  • data rescue
  • error attribution
  • metadata
  • quality control
  • station observations

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