ABSTRACT
Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation of observations by ground-based radars via optical flow techniques or neural network models. Dependent on these radars, typical nowcasting is limited to the regions around their locations. We have developed a method for precipitation nowcasting based on geostationary satellite imagery and incorporated the resulting data into the Yandex.Weather precipitation map (including an alerting service with push notifications for products in the Yandex ecosystem), thus expanding its coverage and paving the way to a truly global nowcasting service.
Supplemental Material
- Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.Google Scholar
- Mikhail Royzner Victor Lamburt Dmitry Solomentsev Svetlana Pospelova Aleksandr Yuzhakov, Pavel Vorobyev. 2017. Method of and system for generating a weather forecast. US Patent US20170299772A1.Google Scholar
- Donny M. Aminou. 2002. MSG's SEVIRI instrument. ESA bulletin. Bulletin ASE. European Space Agency (2002).Google Scholar
- A Bellon and GL Austin. 1978. The evaluation of two years of real-time operation of a short-term precipitation forecasting procedure (SHARP). Journal of Applied Meteorology 17, 12 (1978), 1778--1787.Google ScholarCross Ref
- Neill EH Bowler, Clive E Pierce, and Alan Seed. 2004. Development of a precipitation nowcasting algorithm based upon optical flow techniques. Journal of Hydrology (2004).Google Scholar
- Environmental Modeling Center. 2003. The GFS Atmospheric Model. NCEP Office Note 442, Global Climate andWeather Modeling Branch, EMC, Camp Springs, Maryland (2003).Google Scholar
- Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. 2016. The Cityscapes Dataset for Semantic Urban Scene Understanding. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- DeepGlobe. 2018. DeepGlobe CVPR 2018 workshop and challenge. http: //deepglobe.org/Google Scholar
- Thomas Heinemann, A Latanzio, and Fausto Roveda. 2002. The Eumetsat multisensor precipitation estimate (MPE). In Second International PrecipitationWorking group (IPWG) Meeting. 23--27.Google Scholar
- Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015). Google ScholarDigital Library
- Vladimir Ivashkin and Vadim Lebedev. 2018. Spatiotemporal Data Fusion for Precipitation Nowcasting. arXiv preprint arXiv:1812.10915 (2018).Google Scholar
- Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. 2015. Spatial transformer networks. In Advances in neural information processing systems. 2017-- 2025. Google ScholarDigital Library
- Kaggle. 2017. Dstl Satellite Imagery Feature Detection. https://www.kaggle. com/c/dstl-satellite-imagery-feature-detectionGoogle Scholar
- Pascal Kaiser, Jan DirkWegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, and Konrad Schindler. 2017. Learning aerial image segmentation from online maps. IEEE Transactions on Geoscience and Remote Sensing 55, 11 (2017), 6054-- 6068.Google ScholarCross Ref
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. Interantional Conference on Learning Representations (2014).Google Scholar
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In European conference on computer vision. Springer, 740--755.Google Scholar
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431--3440.Google ScholarCross Ref
- J. S. Marshall and W. McK. Palmer. 1948. The distribution of raindrops with size. J. Meteor. 5 (1948), 165-166.Google Scholar
- Gellért Máttyus, Wenjie Luo, and Raquel Urtasun. 2017. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision. 3438--3446.Google ScholarCross Ref
- Hanna Meyer, Meike Kühnlein, Tim Appelhans, and Thomas Nauss. 2016. Comparison of four machine learning algorithms for their applicability in satellitebased optical rainfall retrievals. Atmospheric Research 169 (2016), 424--433.Google ScholarCross Ref
- World Meteorological Organization. 2015. Manual on Codes - International Codes, Volume I.1, Annex II to the WMO Technical Regulations: part A - Alphanumeric Codes. https://library.wmo.int/index.php?lvl=notice_display&id=13617Google Scholar
- Javier Sánchez Pérez, Enric Meinhardt-Llopis, and Gabriele Facciolo. 2013. TV-L1 optical flow estimation. Image Processing On Line 2013 (2013), 137--150.Google ScholarCross Ref
- Tobias Pohlen, Alexander Hermans, Markus Mathias, and Bastian Leibe. 2017. Full-resolution residual networks for semantic segmentation in street scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4151--4160.Google ScholarCross Ref
- RA Roebeling and I Holleman. 2009. SEVIRI rainfall retrieval and validation using weather radar observations. Journal of Geophysical Research: Atmospheres 114, D21 (2009).Google ScholarCross Ref
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234--241.Google ScholarCross Ref
- Alexander Sergeev and Mike Del Balso. 2018. Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799 (2018).Google Scholar
- Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems. Google ScholarDigital Library
- Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. 2017. Deep learning for precipitation nowcasting: A benchmark and a new model. In Advances in Neural Information Processing Systems. Google ScholarDigital Library
- G. Asrar Y. Furuhama A. Ginati C. Kummerow V. Levizzani A. Mugnai K. Nakamura R. Adler V. Casse M. Cleave M. Debois Smith, E. A. and J. Durning. 2007. International Global Precipitation Measurement (GPM) Program and Mission: An Overview. Springer - Measuring Precipitation from Space - EURAINSAT and the future. Eds. V. Levizzani, P. Bauer, and F. J. Turk 28 (2007), 611-653.Google Scholar
- Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso. 2017. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, 240--248.Google Scholar
- Juanzhen Sun, Ming Xue, James W Wilson, Isztar Zawadzki, Sue P Ballard, Jeanette Onvlee-Hooimeyer, Paul Joe, Dale M Barker, Ping-Wah Li, Brian Golding, et al. 2014. Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bulletin of the American Meteorological Society 95, 3 (2014), 409--426.Google ScholarCross Ref
- Tao Sun, Zehui Chen, Wenxiang Yang, and Yin Wang. 2018. Stacked u-nets with multi-output for road extraction. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 187--1874.Google ScholarCross Ref
- Yumeng Tao, Xiaogang Gao, Alexander Ihler, Soroosh Sorooshian, and Kuolin Hsu. 2017. Precipitation identification with bispectral satellite information using deep learning approaches. Journal of Hydrometeorology 18, 5 (2017), 1271--1283.Google ScholarCross Ref
- Google Trends. 2019. Google Trends. https://g.co/trends/GGij8Google Scholar
- BJ Turner, I Zawadzki, and U Germann. 2004. Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE). Journal of Applied Meteorology 43, 2 (2004), 231--248.Google ScholarCross Ref
- Yandex.Radar. 2019. Global internet analytics Yandex.Radar. https://radar. yandex.ru/top_list?month=2018--12&row_id=yandex-ru-pogoda&offset=10Google Scholar
- A. N. Lukyanov A. A. Shestakova A. A. Shumilin A. V. Travov Yu. B. Pavlyukov, R. B. Zaripov. 2017. The impact of radar data assimilation on atmosphere state analysis in the Moscow region. Russian Meteorology and Hydrology 42 (2017), 357--368. Issue 6.Google ScholarCross Ref
- Christopher Zach, Thomas Pock, and Horst Bischof. 2007. A duality based approach for realtime TV-L 1 optical flow. In Joint Pattern Recognition Symposium. Springer, 214--223. Google ScholarDigital Library
- Zhengxin Zhang, Qingjie Liu, and YunhongWang. 2018. Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters 15, 5 (2018), 749--753.Google ScholarCross Ref
- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, XiaogangWang, and Jiaya Jia. 2017. Pyramid scene parsing network. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2881--2890.Google ScholarCross Ref
Index Terms
- Precipitation Nowcasting with Satellite Imagery
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