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Abstract
Abstract. In the '90s, the Royal Meteorological Institute (RMI) of Belgium started to replace its conventional ''manual'' meteorological network by automated weather stations (AWSs). The meteorological measurement network is now fully automated. RMI counts 18 AWSs that made automated observations centrally available in our headquarters in Uccle, Brussels to internal as well as external users. Due to the large increase in the data amount associated with the automation, quality assurance (QA) procedures are being automated. However, human operators continue to play an essential role in the data validation processes. This contribution describes our newly developed semi-automatic quality control (QC) of 10-min air temperature data. After an existence test, the data are checked for limits consistency, temporal consistency and spatial consistency. At the end of these automated checks, a decision algorithm attributes a flag to each particular data. Each day the QC staff analyzes the preceding day observations in the light of the quality flags assigned by automated QA procedures during the night. It is the human decision whether or not a value is accepted.
Highlights
The value of any meteorological measurement is dependent on the accuracy and precision with which it represents the physical quantity being measured
Literature of the last two decades suggests an evolution toward complex quality assurance (QA) and quality control (QC) in practice with meteorological data processing
A threepronged framework was developed at the Royal Meteorological Institute (RMI) of Belgium for implementing automated component checks on the 10-min air temperature records and decision-making process weighting flags by hierarchical flag type
Summary
The value of any meteorological measurement is dependent on the accuracy and precision with which it represents the physical quantity being measured. The guiding principle is that no decision to flag a datum should be made until all available approaches have been applied toward the assessment of its validity. A threepronged framework was developed at the Royal Meteorological Institute (RMI) of Belgium for implementing automated component checks on the 10-min air temperature records and decision-making process weighting flags by hierarchical flag type. Note that in addition to the RMI’s measurements, automated air temperature records are performed in the synoptic AWSs operated by Belgocontrol, the belgian public company in charge of the air traffic safety in the civil airspace (7 stations, yellow diamonds in Fig. 1) as well as by the Meteorological Wing of the Air Component of Defense (8 stations, green diamonds in Fig. 1) but RMI is not in charge of their data QC. Due to the large heterogeneity within the RMI’s AWSs, five groups based on the recorded air temperature have been distinguished for the automated data QC (see Table 1)
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