Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I-Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II-QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III-the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

Automated summary

The representativeness and quality of collected meteorological data are crucial for the accuracy and precision of climate, hydrological, and biogeochemical analyses. A comprehensive QA/QC statistical framework was developed with three major phases: data exploration, QA of datasets, and development of time series, suitable for both real-time and post-data-collection analysis of meteorological datasets.