Abstract
We present a novel method to identify and locate weather fronts at various pressure levels to create a three dimensional structure using weather data located at the North Atlantic. It provides statistical evaluations regarding the slope and weather phenomena correlated to the identified three dimensional structure. Our approach is based on a deep neural network to locate 2D surface fronts first, which are then used as an initialization to extend them to various height levels. We show that our method is able to detect frontal locations between 500 hPa and 1000 hPa.
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Acknowledgement
The study is supported by the project “Big Data in Atmospheric Physics (BINARY)”, funded by the Carl Zeiss Foundation (grant P2018-02-003). We acknowledge the ECMWF for providing access to the ERA5 reanalysis data and the ZDV of JGU for providing access to Mogon II. We further acknowledge Daniel Kunkel for supporting us with data management and thank Michael Wand for fruitful discussions.
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Niebler, S., Schmidt, B., Tost, H., Spichtinger, P. (2023). Automated Identification and Location of Three Dimensional Atmospheric Frontal Systems. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_1
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DOI: https://doi.org/10.1007/978-3-031-36021-3_1
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