Search

An array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure. Plain Language Summary: The atmospheric profile can be visualized as a three‐dimensional curve representing pressure, temperature, and humidity. By utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphere. In this paper, we confirmed the accuracy of atmospheric profile representation using signatures and introduced the characteristics of signatures revealed through machine‐learning models. The description of profiles by the signature was confirmed with adequate accuracy. Moreover, the model demonstrated robust annual accuracy, with minimal temperature and water vapor discrepancies. It effectively captured the vertical structure and instability of the atmosphere, even during heavy rainfall, characterized by significant temperature and water vapor content fluctuations. Key Points: The utilization possibility of the Signature method for atmospheric profiles was confirmedBy utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphereThe machine‐learning model developed with the signature can predict the profiles with high annual accuracy [ABSTRACT FROM AUTHOR]