Climate analysis & attribution
Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network
In one sentence. U-Net detects stationary fronts around Japan from multi-variable weather fields, reproducing seasonal front shapes on an untrained year except during typhoons.
Abstract
In this study, a stationary front is automatically detected from weather data using a U-Net deep convolutional neural network. The U-Net trained the transformation process from single/multiple physical quantities of weather data to detect stationary fronts using a 10-year data set. As a result of applying the trained U-Net to a 1-year untrained data set, the proposed approach succeeded in detecting the approximate shape of seasonal fronts with the exception of typhoons. In addition, the wind velocity (zonal and meridional components), wind direction, horizontal temperature gradient at 1000 hPa, relative humidity at 925 hPa, and water vapor at 850 hPa yielded high detection performance. Because the shape of the front extracted from each physical quantity is occasionally different, it is important to comprehensively analyze the results to make a final determination.
Labels
- Topic
- Climate analysis & attribution
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.2151/sola.2019-028 OpenAlex:W2956154338
- Added
- 2026-10-22
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