Abstract
Abstract
For over three decades, the Atmospheric Radiation Measurement (ARM) Program user facility has provided researchers with invaluable benchmark atmospheric data. Ensuring the accuracy and integrity of ARM data is vital, and to achieve this, the ARM Data Quality Office (DQO) has implemented customized quality control tests tailored to each variable, with guidance from instrument mentors. These tests are designed to pinpoint common issues, such as data exceeding valid ranges or persisting with little change over extended periods, and ARM offers tools for users to review and exclude contaminated data efficiently. However, certain quality issues, such as spikes in time series or data drift over time, sometimes evade detection by existing tests and require manual identification by data analysts and instrument mentors through visualization tools. To tackle these challenges more efficiently, the DQO has developed and implemented the Iterative Error‐Driven Ensemble Labeling (IEDEL) algorithm with unanimous voting and transfer learning techniques to efficiently generate labeled data at scale. This initiative has empowered the creation of high‐performing machine learning models, enabling real‐time monitoring of data quality issues within the ARM data and thereby enhancing data integrity and accessibility.