AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Gabriela Czibula, Andrei Mihai, Alexandra-Ioana Albu, Istvan-Gergely Czibula, Sorin Burcea and Abdelkader Mezghani
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Gabriela Czibula: Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Andrei Mihai: Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Alexandra-Ioana Albu: Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Istvan-Gergely Czibula: Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Sorin Burcea: Romanian National Meteorological Administration, 013686 Bucharest, Romania
Abdelkader Mezghani: Meteorologisk Instittut, 0371 Oslo, Norway

Mathematics, 2021, vol. 9, issue 14, 1-21

Abstract: Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing A u t o N o w P , a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, A u t o N o w P uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. A u t o N o w P is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of A u t o N o w P . Results showed that A u t o N o w P surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

Keywords: precipitation nowcasting; deep learning; autoencoders; radar data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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