Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 6, pp. 18-22
Development of precipitation nowcasting method using geostationary satellite data
A.I. Andreev
1 , N.I. Pererva
1 , M.O. Kuchma
1 1 Far Eastern Center SRC Planeta, Khabarovsk, Russia
Accepted: 15.09.2020
DOI: 10.21046/2070-7401-2020-17-6-18-22
The paper considers the development of a model for precipitation field nowcasting using the data obtained from the Himawari-8 satellite and a GFS numerical forecast model. The nowcasting method employs a convolutional and recurrent neural network architecture. A peculiarity of the developed model is a possibility to make a forecast using no ground-based meteorological radars data. The authors present preliminary research results as exemplified by the precipitation field nowcasting for a 30-minute period and the 60-minute forecast of the cloud cover optical depth distribution. Finally, the paper outlines the areas for further research with the account to the identified drawbacks of the existing forecasting algorithm software implementation.
Keywords: nowcasting, short-term prediction, precipitations, rain rate, nerual network, Himawari
Full textReferences:
- [1] Agrawal S., Barrington L., Bromberg C., Burge J., Gazen C., Hickey J., Machine Learning for Precipitation Nowcasting from Radar Images, arXiv preprint arXiv:1912.12132, 2019, 6 p., available at: https://arxiv.org/pdf/1912.12132.pdf.
- [2] Lebedev V., Ivashkin V., Rudenko I., Ganshin A., Molchanov A., Ovcharenko S., Grokhovetskiy R., Bushmarinov I., Solomentsev D., Precipitation nowcasting with satellite imagery, 25th ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, Proc., 2019, pp. 2680–2688, available at: https://dl.acm.org/doi/10.1145/3292500.3330762.
- [3] Woo W., Wong W., Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting, Atmosphere, 2017, Vol. 8(3), 48, 20 p., DOI: 10.3390/atmos8030048.
- [4] Xingjian S. H. I., Chen Z., Wang H., Yeung D. Y., Wong W. K., Woo W. C., Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, Advances in Neural Information Processing Systems, 2015, Vol. 28, pp. 802–810, available at: https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf.
- [5] Sirch T., Bugliaro L., Zinner T., Möhrlein M., Vazquez-Navarro M., Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants, Atmospheric Measurement Techniques, 2017, Vol. 10(2), pp. 409–429, available at: https://amt.copernicus.org/articles/10/409/2017/amt-10-409-2017.pdf.
- [6] Liu Y., Xi D. G., Li Z. L., Hong Y., A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach, J. Hydrology, 2015, Vol. 529, pp. 354–364.
- [7] Simonenko E. V., Chudin A. O., Davidenko A. N., The differential method for calculation of cloud motion vectors, Russian Meteorology and Hydrology, 2017, Vol. 42(3), pp. 159–167.
- [8] Zhang W., Han L., Sun J., Guo H., Dai J., Application of multi-channel 3D-cube successive convolution network for convective storm nowcasting, IEEE Intern. Conf. Big Data (BigData), 2019, pp. 1705–1710.
- [9] Akbari Asanjan A., Yang T., Hsu K., Sorooshian S., Lin J., Peng Q., Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks, J. Geophysical Research: Atmospheres, 2018, Vol. 123(22), pp. 12543–12563.
- [10] Sato R., Kashima H., Yamamoto T., Short-term precipitation prediction with skip-connected PredNet, Intern. Conf. Artificial Neural Networks, Proc., 2018, pp. 373–382.
- [11] Lotter W., Kreiman G., Cox D., Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning, arXiv print arXiv:1605.08104, 2016, 18 p., available at: https://arxiv.org/pdf/1605.08104.pdf.
- [12] Alekseeva A. A., Bukharov M. V., Diagnosis of Precipitation and Thunderstorms from Measurements of Outgoing Heat Radiation of a Cloud Cover from Geostationary Satellites, Russian Meteorology and Hydrology, 2005, Vol. 6, pp. 20–26.
- [13] Mirza M., Osindero S., Conditional Generative Adversarial Nets, arXiv preprint arXiv:1411.1784, 2014, 7 p., available at: https://arxiv.org/pdf/1411.1784.pdf.
- [14] Sorokin A. A., Makogonov S. I., Korolev S. P., The Information Infrastructure for Collective Scientific Work in the Far East of Russia, Scientific and Technical Information Proc., 2017, Vol. 4, pp. 302–304.