@article{fdi:010049359,
  title = {{A} procedure for automated quality control and homogenization of historical daily temperature and precipitation data ({APACH}) : part 1 : quality control and application to the {A}rgentine weather service stations},
  author = {{B}oulanger, {J}ean-{P}hilippe and {A}izpuru, {J}. and {L}eggieri, {L}. and {M}arino, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he present paper describes the quality-control component of an automatic procedure ({APACH}: {A} {P}rocedure for {A}utomated {Q}uality {C}ontrol and {H}omogenization of {W}eather {S}tation {D}ata) developed to control quality and homogenize the historical daily temperature and precipitation data from meteorological stations. {T}he quality-control method is based on a set of decision-tree algorithms analyzing separately precipitation and minimum and maximum temperature. {A}ll our tests are non-parametric and therefore are potentially useful in regions or countries presenting different climates as those observed in {A}rgentina. {T}he method is applied to the 1959-2005 historical daily database of the {A}rgentine {N}ational {W}eather {S}ervice. {O}ur results are coherent with the history of the {W}eather {S}ervice and more specifically with the history of implementation of systematized quality control processes. {I}n temperature, our method detects a larger number of suspect values before 1967 (when there was no quality control) and after 1997 (when only real-time quality control had been applied). {I}n precipitation, the detection of error in extreme precipitations is complex, but our method clearly detected a strong decrease in the number of potential outliers after 1976 when the {N}ational {W}eather {S}ervice was militarized, and the network was strongly reduced, focusing more on airport weather stations. {A}lso in precipitation, we analyze in detail the long dry sequences and are able to identify potential long erroneous sequences. {T}his is important for the use of the data for hydrological or agricultural impact studies. {F}inally, all the data are flagged with codes representing the path followed by the record in our decision-tree algorithms. {W}hile each code is associated to one of the categories ("{U}seful", "{N}eed-{C}heck", "{D}oubtful" or "{S}uspect"), the final user is free to redefine such category-assignment.},
  keywords = {},
  booktitle = {},
  journal = {{C}limatic {C}hange},
  volume = {98},
  numero = {3-4},
  pages = {471--491},
  ISSN = {0165-0009},
  year = {2010},
  DOI = {10.1007/s10584-009-9741-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010049359},
}