This paper presents a weather map prediction method using RGB metaphorical feature extraction for atmospheric pressure patterns. In the field of meteorological science, predicting weather based on the analysis of observational data and the knowledge of weather experts is crucial. Weather experts draw weather maps based on air pressure distribution; hence, we believe that weather maps entail the interpretations of weather experts. In this study, we improved the prediction accuracy by using machine learning to recognize patterns of qualitative expert interpretations that cannot be predicted by analyzing observed data alone. The proposed method can be realized via two steps. The first is developing a module for extracting pressure pattern features from a weather map. Certain features, such as tropical cyclones or atmospheric high/low pressure distributions, are emphasized in weather maps to facilitate better understanding of the weather features. Therefore, we can predict weather features based on the knowledge of weather experts using data that contain their interpretations, particularly weather maps. The developed module extracts the atmospheric pressure features from the current weather map as an RGB metaphorical gradation map. The second step is developing a module to design a predicted weather map using the extracted features. The weather map of the following day is predicted using pix2pix. To the best of our knowledge, our method for extracting features from weather maps is the first to create a predicted weather map automatically.

diorama table is an interactive table installation. People place physical objects on the table and projected elements such as trains, cars, houses, and trees appear and are interacted with physical objects.