DEVELOPMENT OF MINUTE SCALE TEMPORAL RESOLUTION DATA QUALITY CONTROL METHOD FROM AUTOMATIC WEATHER STATION (CASE STUDY BANDUNG DISTRICT)

• Main Author: Tryanda, Raffandy
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/63743
• Institution: Institut Teknologi Bandung

The Automatic Weather Station (AWS) has been installed in Bandung Regency since 2018. The AWS data has never been quality controlled. The Quality Control (QC) method developed in sufficient detail by previous research on the Global Historical Climate Network (GHCN) station using daily scale data. While the data contained in AWS is high temporal data with a minute scale. With this published method, it is necessary to develop a QC method for minute scale data found on AWS in Bandung Regency. The use of this QC method is a development of a previously published method. The methods used and developed are duplication and repetition check, outlier check, internal and temporal check, and spatial consistency check. All of these method algorithms were developed for high-scale resolution data. In this case the development is carried out for data per 5 minutes. In the QC process there are several old methods are reduced due to their irrelevant use in minute scale data, especially in the tropics. Based on the simulation results in this study, minute scale QC shows more flagged data detected than daily scale QC. So with the development of the method, it can make the data more accurate. The QC results state that the temperature and wind speed parameters have consistent data throughout the year with an average value ranging from 1-2% of the data declared flagged, while the rainfall parameters tend to be inconsistent and even reach flagged data of >30%..