A procedure for automated quality control and homogenization of historical daily temperature and precipitation data (APACH): Part 1: Quality control and application to the Argentine weather service stations. (Special Issue: The 6th European Framework Programme CLARIS Project: a Europe-South America Network for climate change assessment and impact studies.)

摘要

The present paper describes the quality-control component of an automatic procedure (APACH: A Procedure for Automated Quality Control and Homogenization of Weather Station Data) developed to control quality and homogenize the historical daily temperature and precipitation data from meteorological stations. The quality-control method is based on a set of decision-tree algorithms analyzing separately precipitation and minimum and maximum temperature. All our tests are non-parametric and therefore are potentially useful in regions or countries presenting different climates as those observed in Argentina. The method is applied to the 1959-2005 historical daily database of the Argentine National Weather Service. Our results are coherent with the history of the Weather Service and more specifically with the history of implementation of systematized quality control processes. In 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). In 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 National Weather Service was militarized, and the network was strongly reduced, focusing more on airport weather stations. Also in precipitation, we analyze in detail the long dry sequences and are able to identify potential long erroneous sequences. This is important for the use of the data for hydrological or agricultural impact studies. Finally, all the data are flagged with codes representing the path followed by the record in our decision-tree algorithms. While each code is associated to one of the categories ("Useful", "Need-Check", "Doubtful" or "Suspect"), the final user is free to redefine such category-assignment.