Quality Control (QC) and Quality Assurance (QA) Procedures for Meteorological Data from Automatic Weather Stations

Meteorological Data is received from automatic weather stations in a raw format. Quality control (QC) and Quality Assurance (QA) are vital for effectively utilizing meteorological information, ensuring reliability and precision for diverse applications. High-quality data is indispensable for scientists, engineers, and decision-makers who rely on accurate meteorological insights for improved weather prediction, research, planning, and decision-making processes. This study presents and puts into practice extensive quality check and assurance techniques to assess meteorological data from Automatic Weather Station (AWS) networks that are adequately maintained year-round and have their performance cross-checked with traveling standards. These procedures include validation of data record structures, range and limit checks, temporal and internal consistency assessments, persistence tests, and spatial consistency evaluations. This approach identifies multiple error types and facilitates corrective actions such as preventive or corrective maintenance, bias corrections, calibration, sensor replacements, and data exclusions. This study investigated the QA/QC of data from 13 AWSs collected between January 2023 to October 2024 from the Pune Districts of Maharashtra, India. The performance evaluation of automatic weather stations (A WS) through range and consistency checks demonstrates high reliability, with average data validity exceeding 95% across key parameters, indicating their effectiveness for accurate meteorological monitoring.