<![CDATA[Automatic observation data with high spatial and temporal resolution is very useful for knowing weather conditions in real time and also used to improve forecasts using assimilated data from this data. The purpose of this paper is to determine the quality of AWS data using the reliability test method and the internal consistency test. The results of data quality testing show the average percentage of suspect data from the nine parameters, namely 6.4. This shows that there are still several problems related to the suspect data and missing data. It is necessary to have a standard quality assurance system which regulates about quality assurance starting from selecting location, selecting standard sensors, good communication network support, the availability of a data Quality Control (QC) monitoring system, and realtime data QC procedures that are implemented both in raw QC. data (site level / basic QC) up to QC processing level at the center.]]>
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