| Title |
Nowcasting precipitation using weather radar data for Lithuania: the first results / |
| Authors |
Čiurlionis, Aivaras ; Lukoševičius, Mantas |
| Full Text |
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| Is Part of |
CEUR workshop proceedings: System 2018: Symposium for young scientists in technology, engineering and mathematics: proceedings of the symposium for young scientists in technology, engineering and mathematics, Gliwice, Poland, May 28, 2018. / edited by G. Capizzi, R. Damaševičius, A. Lopata, T. Krilavičius, Ch. Napoli, M. Woźniak.. Aachen : CEUR-WS. 2018, vol. 2147, p. 55-60.. ISSN 1613-0073 |
| Keywords [eng] |
forecast ; meteorology ; nowcasting ; precipitation |
| Abstract [eng] |
Although the accuracy and the duration of modern weather forecasts constantly increase together, numerical weather prediction methods still face a few drawbacks. Due to an extensive computing time and a high power usage, these methods are unable to efficiently react to rapidly changing initial weather conditions. Also, most of the numerical weather prediction models can be less accurate for smaller regions with specific local weather conditions. These problems are addressed by a technique called nowcasting, which uses an extrapolation of various current weather conditions. Multiple research papers have shown that this technique can outperform traditional weather predictions for up to two hours. Furthermore, it can be improved using machine learning algorithms. In this paper nowcasting algorithms are used to predict a short-term precipitation over Lithuania using weather radar images provided by Lithuanian Hydrometeorology service. A Hanssen-Kuipers score is used to evaluate the accuracy of prediction against observed precipitation maps. The results of three extrapolation algorithms (basic translation, step translation, and sequence translation) and a single machine learning algorithm based on convolutional neural networks (CNN) are evaluated for two chosen hours and compared to the persistency algorithm. The average scores of each prediction algorithm for a single week are also presented. Although the results remain accurate for up to 45 minutes only, the accuracy can be improved by adding additional variables to the extrapolation. The better accuracy can also be achieved by using more sophisticated machine learning algorithms, like recurrent neural networks and their variations, that take dependencies on previous inputs in time series into account. This paper presents the first results of the algorithms, which are to be improved by further research. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2018 |
| CC license |
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