Radar-based rainfall prediction using generative adversarial network

Seongsim Yoon; Hongjoon Shin; Jae-Yeong Heo

doi:10.3741/JKWRA.2023.56.8.471

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2023 Vol.56, Issue 8 Next Page

Research Article

Radar-based rainfall prediction using generative adversarial network 적대적 생성 신경망을 이용한 레이더 기반 초단시간 강우예측

31 August 2023. pp. 471-484

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Abstract

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

Keywords

Radar

Rainfall prediction

Generative adversarial neural networks

Deep learning

적대적 생성 신경망 기반의 딥러닝 모델은 학습된 정보를 바탕으로 새로운 정보를 생성하는데 특화되어 있다. 구글 딥마인드에서 개발한 deep generative model of rain (DGMR) 모델은 대규모 레이더 이미지 데이터의 복잡한 패턴과 관계를 학습하여, 예측 레이더 이미지를 생성하는 적대적 생성 신경망 모델이다. 본 연구에서는 환경부 레이더 강우관측자료를 이용하여 DGMR 모델을 학습하고, 2021년 8월 호우사례를 대상으로 적대적 생성 신경망을 이용하여 강우예측을 수행하고 기존 예측기법들과 정확도를 비교하였다. DGMR은 대체적으로 선행 60분까지는 강우 분포 위치가 관측강우와 가장 유사하였으나, 전체 영역에서 강한 강우가 발생한 사례에서는 강우가 지속적으로 발달하는 것으로 예측하는 경향이 있었다. 통계적 평가에서도 DGMR 기법이 1시간 선행예측에서 임계성공지수 0.57~0.79, 평균절대오차 0.57~1.36 mm로 나타나 타 기법 대비 효과적인 강우예측 기법임을 보여주었다. 다만, 생성 결과의 다양성이 부족한 경우가 발생하여 예측 정확도를 저하하므로 다양성을 개선하기 위한 연구와 2시간 이상의 선행예측에 대한 정확도 개선을 위해 물리기반 수치예보모델 예측강우 자료를 이용한 보완이 필요할 것으로 판단되었다.

키워드

레이더

강우예측

적대적 생성 신경망

딥러닝

References

Ayzel, G., Scheffer, T., and Heistermann, M. (2020). "RainNet v1.0: A convolutional neural network for radar-based precipitation nowcasting." Geoscientific Model Development, Vol. 13, pp. 2631-2644. 10.5194/gmd-13-2631-2020

Choi, S., and Kim, Y. (2022). "Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains." Geoscientific Model Development, Vol. 15, No. 15, pp. 5967-5985. doi: 10.5194/gmd-15-5967-2022. 10.5194/gmd-15-5967-2022

Clark, A., Donahue, J., and Simonyan, K. (2019). "Adversarial video generation on complex datasets." arXiv:1907.06571. doi: 10.48550/arXiv.1907.06571. 10.48550/arXiv.1907.06571

Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J., Bell, A., and Kalchbrenner, N., (2022). "Deep learning for twelve hour precipitation forecasts." Nature Communications, Vol. 13, 5145. doi: 10.1038/s41467-022-32483-x. 10.1038/s41467-022-32483-x36050311PMC9436943

Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). "Progressive growing of gans for improved quality, stability, and variation." arXiv preprint arXiv:1710.10196.

Kim, Y., and Hong, S. (2022). "Very short-term rainfall prediction using ground radar observations and conditional generative adversarial networks," IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 4104308. doi: 10.1109/TGRS.2021.3108812. 10.1109/TGRS.2021.3108812

Lin, C., Vasic, S., Kilambi, A., Turner, B., and Zawadzki, I. (2005). "Precipitation forecast skill of numerical weather prediction mod?els and radar nowcasts." Geophysical Research Letters, Vol. 32, No. 14, L14801, doi: 10.1029/2005GL023451. 10.1029/2005GL023451

Palmer, T.N., and Räisänen, J. (2002). "Quantifying the risk of extreme seasonal precipitation events in a changing climate." Nature, Vol. 415, pp. 512-514. doi: 10.1038/415512a. 10.1038/415512a11823856

Ravuri, S., Lenc, K., Willson, M, Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., and Mohamed, S. (2021). "Skilful precipitation nowcasting using deep generative models of radar." Nature, Vol. 597, pp. 672-677. doi: 10.1038/s41586-021-03854-z. 10.1038/s41586-021-03854-z34588668PMC8481123

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat. (2019). "Deep learning and process understanding for data-driven Earth system science." Nature, Vol. 566, pp. 195-204. doi: 10.1038/s41586-019-0912-1. 10.1038/s41586-019-0912-130760912

Richardson, D.S. (2000). " Skill and relative economic value of the ECMWF ensemble prediction system." Quarterly Journal of the Royal Meteorological Society, Vol. 126, pp. 649-667. 10.1002/qj.49712656313

Seo, H.Y., Lee, G.H., Hyein, M.S., and Won, Y.S. (2020). "Current status of improvement of rainfall radar utilization." Water for future, Korea Water Resources Association, Vol. 53, p. 35-45.

Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W. (2015). "Convolutional LSTM Network: A machine learning approach for precipitation nowcasting." In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (NIPS'15), Montreal, Canada, MIT Press, Cambridge, MA, U.S., pp. 802-810.

Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung D., Wong, W., and Woo, W. (2017). "Deep learning for precipitation nowcasting: A benchmark and a new model." 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, U.S.

Shin, H.J., Yoon, S.S., and Choi, J.M. (2021). "Radar rainfall prediction based on deep learning considering temporal consistency." Journal of Korea Water Resources Association, Vol. 54, No. 5, pp. 301-309. 10.3741/JKWRA.2021.54.5.301

Sun, J., Xue, M., Wilson, J.W., Zawadzki, I., Ballard, S.P., OnvleeHooimeyer, J., Joe, P., Barker, D.M., Li, P.-W., Golding, B., Xu, M., and Pinto, J. (2014). "Use of NWP for nowcasting convective precipitation: recent progress and challenges." Bulletin of the American Meteorological Society, Vol. 95, No. 3 pp. 409-426, doi: 10.1175/BAMS-D-11-00263.1. 10.1175/BAMS-D-11-00263.1

Tran, Q.K., and Song, S.K. (2019). "Computer vision in precipitation nowcasting: applying image quality assessment metrics for training deep neural networks." Atmosphere, Vol. 10, No. 5, 244. doi: 10.3390/atmos10050244. 10.3390/atmos10050244

Turner, B.J., Zawadzki, I., and Germann, U. (2004). "Predictability of precipitation from continental radar images. Part III: Operational Nowcasting Implementation (MAPLE)." Journal of Applied Meteorology, Vol. 43, No. 2, pp. 231-248. 10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2

Xie, P.F., Li, X.T., Ji, X.Y., Chen, X.L., Chen, Y.Z., Liu, J., and Ye, Y.M. (2022). "An energy-based generative adversarial forecaster for radar echo map extrapolation." IEEE Geoscience and Remote Sensing Letters, Vol. 19, 3500505. doi: 10.1109/lgrs.2020.3023950. 10.1109/LGRS.2020.3023950

Yoon, S.S., Park, H.S., and Shin, H.J. (2020). "Very short-term rainfall prediction based on radar image learning using deep neural network." Journal of Korea Water Resources Association, Vol. 53, No. 12, pp. 1159-1172.

Zhang, Y., Long, M., Chen, K, Xing, L., Jin, R., Jordan, M.I., and Wang, J. (2023). "Skilful nowcasting of extreme precipitation with NowcastNet." Nature, Vol. 619, pp. 526-532. doi: 10.1038/s41586-023-06184-4 10.1038/s41586-023-06184-437407824PMC10356617

Zheng, K., Liu, Y., Zhang, J.B., Luo, C., Tang, S.Y., Ruan, H.H., Tan, Q.Y., Yi, Y.L., and Ran, X.T. (2022). "GAN-argcPredNet v1.0: A generative adversarial model for radar echo extrapolation based on convolutional recurrent units." Geoscientific Model Development, Vol. 15, No. 4, pp. 1467-1475. doi: 10.5194/ gmd-15-1467-2022. 10.5194/gmd-15-1467-2022

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 56
No :8
Pages :471-484
Received Date : 2023-04-18
Revised Date : 2023-07-20
Accepted Date : 2023-07-27
DOI :https://doi.org/10.3741/JKWRA.2023.56.8.471

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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