Precipitation Nowcasting using Data Augmentation

• Eduardo Bezerra Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Augusto Fonseca Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) http://orcid.org/0000-0003-1480-5814
• Adriano Cabo Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Fabio Porto National Laboratory for Scientific Computing (LNCC)
• Mariza Ferro Fluminense Federal University (UFF)

DOI: https://doi.org/10.5753/sbbd_estendido.2023.25647

Abstract

This paper proposes a simple data augmentation technique specifically designed to mitigate the data unbalancing problem in precipitation nowcasting. We consider the existence of one or more observational systems, each one comprised of a set of (either weather or rain gauge) stations. We use simulated data coming from the ERA5 numerical model to complement precipitation observations made by rain gauge stations, and use the resulting synthetic observations to augment data for a given weather station. We present preliminary results training a machine learning model using this data augmentation technique. These results show that the technique can be useful to improve the predictive performance of the resulting forecasting model.

Keywords: precipitation nowcasting, machine learning, data augmentation

References

Vladimir Braverman. Sliding Window Algorithms, pages 2006–2011. Springer New York, New York, NY, 2016. ISBN 978-1-4939-2864-4. doi: 10.1007/978-1-4939-2864-4_797.

Hans Hersbach et al. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020. doi: https://doi.org/10.1002/qj.3803.

Herrera Montano et al. Survey of techniques on data leakage protection and methods to address the insider threat. Cluster Computing, 25(6):4289–4302, 2022. ISSN 1573-7543. doi: 10.1007/s10586-022-03668-2. RIa.