Authors:
Andreea Ploscar
;
Anca Muscalagiu
;
Eduard Pauliuc
and
Adriana Coroiu
Affiliation:
Department of Computer Science, Babeș-Bolyai University, M. Koganiceanu 1 Street, Cluj-Napoca, Romania
Keyword(s):
Atmospherical Fronts, Detection, Classification, Convolutional Neural Network.
Abstract:
This paper presents an application that uses Convolutional Neural Networks (CNN) for the automatic detection and classification of atmospherical fronts in synoptic maps, which are a graphical representation of weather conditions over a specific geographic area at a given point in time. These fronts are significant indicators of meteorological characteristics and are essential for weather forecasting. The proposed method takes in a region extracted from a synoptic map to detect and classify fronts as cold, warm, or mixed, setting our study apart from existing literature. Furthermore, unlike previous research that typically utilizes atmospheric data grids, our study employs synoptic maps as input data. Additionally, our model produces a single output, accurately representing the front type with a 78% accuracy rate. The CNN model was trained on data collected from various meteorological stations worldwide between 2013 and 2022. The proposed tool can provide valuable information to weath
er forecasters and improve their accuracy.
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