Browse > Article
http://dx.doi.org/10.1633/JISTaP.2022.10.S.13

Construction of a Spatio-Temporal Dataset for Deep Learning-Based Precipitation Nowcasting  

Kim, Wonsu (Korea Institute of Science and Technology Information (KISTI))
Jang, Dongmin (Korea Institute of Science and Technology Information (KISTI))
Park, Sung Won (Korea Institute of Science and Technology Information (KISTI))
Yang, MyungSeok (Korea Institute of Science and Technology Information (KISTI))
Publication Information
Journal of Information Science Theory and Practice / v.10, no.spc, 2022 , pp. 135-142 More about this Journal
Abstract
Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.
Keywords
artificial intelligence; deep learning; numerical weather prediction; precipitation nowcasting; preprocessing; KISTI Dataset for Rainfall Prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kwon, S., Jung, S. H., & Lee, G. W. (2015). Inter-comparison of radar rainfall rate using Constant Altitude Plan Position Indicator and hybrid surface rainfall maps. Journal of Hydrology, 531(Pt 2), 234-247. https://doi.org/10.1016/j.jhydrol.2015.08.063.   DOI
2 Lorenz, E. N. (1982). Atmospheric predictability experiments with a large numerical model. Tellus, 34(6), 505-513. https://doi.org/10.3402/tellusa.v34i6.10836.   DOI
3 Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 28, 802-810. https://proceedings.neurips.cc/paper/2015/hash/07563a3fe3bbe7e3ba84431ad9d055af-Abstract.html.
4 Hong, S. Y. (2004). Comparison of heavy rainfall mechanisms in Korea and the central US. Journal of the Meteorological Society of Japan, 82(5), 1469-1479. https://doi.org/10.2151/jmsj.2004.1469.   DOI
5 Hong, S. Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134(9), 2318-2341. https://doi.org/10.1175/MWR3199.1.   DOI
6 Jeong, C. H., Kim, W., Joo, W., Jang, D., & Yi, M. Y. (2021). Enhancing the encoding-forecasting model for precipitation nowcasting by putting high emphasis on the latest data of the time step. Atmosphere, 12(2), 261. https://doi.org/10.3390/atmos12020261.   DOI
7 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., & Mohamed, S. (2021). Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878), 672-677. https://doi.org/10.1038/s41586-021-03854-z.   DOI
8 Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X. Y., Wang, W., & Powers, J. G. (2008). A description of the advanced research WRF version 3. University Corporation for Atmospheric Research.
9 Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2017). Deep learning for precipitation nowcasting: A benchmark and a new model. Advances in Neural Information Processing Systems, 30, 5618-5628. https://proceedings.neurips.cc/paper/2017/hash/a6db4ed04f1621a119799fd3d7545d3d-Abstract.html.
10 Sibson, R. (1981, March 24-27). A brief description of natural neighbour interpolation. In V. Barnett (Ed.), Interpreting multivariate data: Proceedings of the conference entitled 'looking at multivariate data' held in the University of Sheffield, U.K. from 24-27 March 1980 (pp. 21-36). John Wiley & Sons.
11 Song, H. J., Lim, B., & Joo, S. (2019). Evaluation of rainfall forecasts with heavy rain types in the high-resolution unified model over South Korea. Weather and Forecasting, 34(5), 1277-1293. https://doi.org/10.1175/WAF-D-18-0140.1.   DOI
12 Dixon, M., & Wiener, G. (1993). TITAN: Thunderstorm identification, tracking, analysis, and nowcasting-a radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10(6), 785-797. https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.   DOI
13 Shin, H. C., Ha, J. H., Ahn, K. D., Lee, E. H., Kim, C. H., Lee, Y. H., & Clayton, A. (2022). An overview of KMA's operational NWP data assimilation systems. In S. K. Park, & L. Xu (Eds.), Data assimilation for atmospheric, oceanic and hydrologic applications (Vol. IV) (pp. 665-687). Springer International Publishing.
14 Sonderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., & Kalchbrenner, N. (2020). MetNet: A neural weather model for precipitation forecasting. ArXiv. https://doi.org/10.48550/arXiv.2003.12140.
15 Turnera, B. J., Zawadzki, I., & Germann, U. (2004). Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE). Journal of Applied Meteorology, 43(2), 231-248. https://doi.org/10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2.   DOI
16 Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P., Onvlee-Hooimeyer, J., Joe, P., Barker, D. M., Li, P. W., Golding, B., Xu, M., & Pinto, J. (2014). Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bulletin of the American Meteorological Society, 95(3), 409-426. https://doi.org/10.1175/BAMS-D-11-00263.1.   DOI
17 Wolfson, M. M., & Clark, D. A. (2006). Advanced aviation weather forecasts. Lincoln Laboratory Journal, 16(1), 31-58. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.163.920&rep=rep1&type=pdf.
18 Germann, U., & Zawadzki, I. (2004). Scale dependence of the predictability of precipitation from continental radar images. Part II: Probability forecasts. Journal of Applied Meteorology, 43(1), 74-89. https://doi.org/10.1175/1520-0450(2004)043<0074:SDOTPO>2.0.CO;2.   DOI
19 Ho, C. H., Lee, J. Y., Ahn, M. H., & Lee, H. S. (2003). A sudden change in summer rainfall characteristics in Korea during the late 1970s. International Journal of Climatology, 23(1), 117-128. https://doi.org/10.1002/joc.864.   DOI
20 Bellon, A., Zawadzki, I., Kilambi, A., Lee, H. C., Lee, Y. H., & Lee, G. (2010). McGill algorithm for precipitation now-casting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique. Asia-Pacific Journal of Atmospheric Sciences, 46(3), 369-381. https://doi.org/10.1007/s13143-010-1008-x.   DOI
21 Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-317. https://doi.org/10.1016/j.rser.2017.04.018.   DOI
22 Imran, M., Castillo, C., Lucas, J., Meier, P., & Vieweg, S. (2014, April 7-11). AIDR: Artificial intelligence for disaster response. WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web (pp. 159-162). Association for Computing Machinery.
23 Han, J. H., Suh, M. S., Yu, H. Y., & Roh, N. Y. (2020). Development of fog detection algorithm using GK2A/AMI and ground data. Remote Sensing, 12(19), 3181. https://doi.org/10.3390/rs12193181.   DOI
24 Hoffman, R. N., Kumar, V. K., Boukabara, S. A., Ide, K., Yang, F., & Atlas, R. (2018). Progress in forecast skill at three leading global operational NWP centers during 2015-17 as seen in summary assessment metrics (SAMs). Weather and Forecasting, 33(6), 1661-1679. https://doi.org/10.1175/WAFD-18-0117.1.   DOI
25 Hong, S. Y., Kwon, Y. C., Kim, T. H., Esther Kim, J. E., Choi, S. J., Kwon, I. H., Kim, J., Lee, E. H., Park, R. S., & Kim, D. I. (2018). The Korean Integrated Model (KIM) system for global weather forecasting. Asia-Pacific Journal of Atmospheric Sciences, 54 Suppl 1, 267-292. https://doi.org/10.1007/s13143-018-0028-9.   DOI
26 Hong, S. Y., & Lim, J. O. J. (2006). The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42(2), 129-151. https://www.dbpia.co.kr/journal/articleDetail?nodeId=node00937254.
27 Kim, J. H., Yang, J. G., Kim, S. H., & Kim, J. S. (2012). Enhancement of Korean weather radar capability by introducing a dual-pol radar network. Paper presented at WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation, Brussels, Belgium.
28 Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018). Deep learning and its applications in biomedicine. Genomics, Proteomics and Bioinformatics, 16(1), 17-32. https://doi.org/10.1016/j.gpb.2017.07.003.   DOI
29 Chung, S. R., Ahn, M. H., Han, K. S., Lee, K. T., & Shin, D. B. (2020). Meteorological products of Geo-KOMPSAT 2A (GK2A) satellite. Asia-Pacific Journal of Atmospheric Sciences, 56(2), 185. https://doi.org/10.1007/s13143-020-00199-x.   DOI
30 Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 113(13), D13103. https://doi.org/10.1029/2008JD009944.   DOI
31 Kain, J. S. (2004). The Kain-Fritsch convective parameterization: An update. Journal of Applied Meteorology, 43(1), 170-181. https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.   DOI
32 Han, L., Sun, J., & Zhang, W. (2020). Convolutional neural network for convective storm nowcasting using 3-D doppler weather radar data. IEEE Transactions on Geoscience and Remote Sensing, 58(2), 1487-1495. https://doi.org/10.1109/TGRS.2019.2948070.   DOI
33 Evans, J. E., & Ducot, E. R. (2006). Corridor integrated weather system. Lincoln Laboratory Journal, 16(1), 59-80. https://www.ll.mit.edu/r-d/publications/corridor-integratedweather-system.
34 Germann, U., & Zawadzki, I. (2002). Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Monthly Weather Review, 130(12), 2859-2873. https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2.   DOI