Augmenting Weather Sensor Data with Remote Knowledge Graphs

The latest analytical models are becoming frequently used in meteorological science research. For instance, machine learning and deep learning models are being trained for weather forecasting. A solid machine learning model can give trustworthy findings that aid individuals in making weather-related decisions. However, the performance of analytical models is largely determined not only by the design of the model body but also by the input features. We address common issues of modern meteorological studies that take sensor data as the input for various analytical models. In contrast to the traditional practice of combining and preprocessing many fixed sensor data bulks to create an augmented dataset, we tunnel into remote knowledge graphs to fetch and augment the sensor data in a scalable way. As a consequence, we reduce the amount of time and storage space to preprocess diverse data in preparation for analytical models leveraging the high interoperability between knowledge graphs.