[1] |
BURTON R R, BLYTH A M, CUI Z, et al. Satellite-based nowcasting of West African mesoscale storms has skill at up to 4-h lead time[J]. Weather and Forecasting, 2022, 37(4): 445–455, https://doi.org/10.1175/waf-d-21-0051.1 |
[2] |
DOUGLAS I, ALAM K, MAGHENDA M, et al. Unjust waters: Climate change, flooding and the urban poor in Africa[J]. Environment and Urbanization, 2008, 20(1): 187–205, https://doi.org/10.1177/0956247808089156 |
[3] |
PULKKINEN S, NERINI D, PÉREZ HORTAL A A, et al. Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1)[J]. Geoscientific Model Development, 2019, 12(10): 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019 |
[4] |
WILSON J W, CROOK N A, MUELLER C K, et al. Nowcasting thunderstorms: A status report[J]. Bulletin of the American Meteorological Society, 1998, 79(10): 2079–2100, https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2 doi: 10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2 |
[5] |
SUN J, XUE M, WILSON J W, et al. Use of NWP for nowcasting convective precipitation: Recent progress and challenges[J]. Bulletin of the American Meteorological Society, 2014, 95(3): 409–426, https://doi.org/10.1175/BAMS-D-11-00263.1 |
[6] |
GOLDING B. Nimrod: A system for generating automated very short range forecasts[J]. Meteorological Applications, 1998, 5(1): 1–16, https://doi.org/10.1017/S1350482798000577 |
[7] |
CUO L, PAGANO T C, WANG Q. A review of quantitative precipitation forecasts and their use in short- to mediumrange streamflow forecasting[J]. Journal of Hydrometeorology, 2011, 12(5): 713–728, https://doi.org/10.1175/2011JHM1347.1 |
[8] |
BROWNING K, COLLIER C. Nowcasting of precipitation systems[J]. Reviews of Geophysics, 1989, 27(3): 345–370, https://doi.org/10.1029/RG027i003p00345 |
[9] |
CHEN L, CAO Y, MA L, et al. A deep learning-based methodology for precipitation nowcasting with radar[J]. Earth and Space Science, 2020, 7(2): e2019EA000812, https://doi.org/10.1029/2019EA000812 |
[10] |
LIN C, VASIĆ S, KILAMBI A, et al. Precipitation forecast skill of numerical weather prediction models and radar nowcasts[J]. Geophysical Research Letters, 2005, 32(14): L14801, https://doi.org/10.1029/2005GL023451 |
[11] |
KOTSUKI S, KUROSAWA K, OTSUKA S, et al. Global precipitation forecasts by merging extrapolation-based nowcast and numerical weather prediction with locally optimized weights[J]. Weather and Forecasting, 2019, 34 (3): 701–714, https://doi.org/10.1175/WAF-D-18-0164.1 |
[12] |
WILSON J, MEGENHARDT D, PINTO J. NWP and radar extrapolation: Comparisons and explanation of errors[J]. Monthly Weather Review, 2020, 148(12): 4783–4798, https://doi.org/10.1175/MWR-D-20-0221.1 |
[13] |
ZAHRAEI A, HSU K-L, SOROOSHIAN S, et al. Short-term quantitative precipitation forecasting using an object-based approach[J]. Journal of Hydrology, 2013, 483: 1–15, https://doi.org/10.1016/j.jhydrol.2012.09.052 |
[14] |
VILA D A, MACHADO L A T, LAURENT H, et al. Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) using satellite infrared imagery: Methodology and validation[J]. Weather and Forecasting, 2008, 23(2): 233–245, https://doi.org/10.1175/2007WAF2006121.1 |
[15] |
BERENGUER M, SEMPERE-TORRES D, PEGRAM G G. SBMcast–An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation[J]. Journal of Hydrology, 2011, 404(3–4): 226–240, https://doi.org/10.1016/j.jhydrol.2011.04.033 |
[16] |
BECHINI R, CHANDRASEKAR V. An enhanced optical flow technique for radar nowcasting of precipitation and winds[J]. Journal of Atmospheric and Oceanic Technology, 2017, 34(12): 2637–2658, https://doi.org/10.1175/JTECHD-17-0110.1 |
[17] |
WANG C, WANG P, WANG D, et al. Nowcasting multicell short-term intense precipitation using graph models and random forests[J]. Monthly Weather Review, 2020, 148(11): 4453–4466, https://doi.org/10.1175/MWR-D-20-0050.1 |
[18] |
MAO Y, SORTEBERG A. Improving radar-based precipitation nowcasts with machine learning using an approach based on random forest[J]. Weather and Forecasting, 2020, 35(6): 2461–2478, https://doi.org/10.1175/WAF-D-20-0080.1 |
[19] |
YU X, ZHOU X, WANG X. The advances in the nowcasting techniques on thunderstorms and severe convection[J]. Acta Meteorologica Sinica, 2012, 70(3): 311–337, https://doi.org/10.1007/s11783-011-0280-z |
[20] |
GAGNE D J, MCGOVERN A, HAUPT S E, et al. Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles[J]. Weather and Forecasting, 2017, 32(5): 1819–1840, https://doi.org/10.1175/WAF-D-17-0010.1 |
[21] |
ZHANG P, ZHANG L, LEUNG H, et al. A deep-learning based precipitation forecasting approach using multiple environmental factors[C]// 2017 IEEE International Congress on Big Data. Boston: IEEE, 2017. |
[22] |
INOUE T, MISUMI R. Learning from precipitation events in the wider domain to improve the performance of a deep learning-based precipitation nowcasting model[J]. Weather and Forecasting, 2022, 37(6): 1013–1026, https://doi.org/10.1175/WAF-D-21-0078.1 |
[23] |
ESPEHOLT L, AGRAWAL S, SØNDERBY C, et al. Deep learning for twelve hour precipitation forecasts[J]. Nature Communications, 2022, 13(1): 5145, https://doi.org/10.1038/s41467-022-32483-x |
[24] |
LEINONEN J, HAMANN U, SIDERIS I V, et al. Thunderstorm nowcasting with deep learning: A multi-hazard data fusion model[J]. Geophysical Research Letters, 2023, 50(8): e2022GL101626, https://doi.org/10.1029/2022GL101626 |
[25] |
HARRIS L, MCRAE A T, CHANTRY M, et al. A generative deep learning approach to stochastic downscaling of precipitation forecasts[J]. Journal of Advances in Modeling Earth Systems, 2022, 14(10): e2022MS003120, https://doi.org/10.1029/2022MS003120 |
[26] |
CHEN G, WANG W C. Short-term precipitation prediction for contiguous United States using deep learning[J]. Geophysical Research Letters, 2022, 49(8): e2022GL097904, https://doi.org/10.1029/2022GL097904 |
[27] |
YAO H, TANG X, WEI H, et al. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction [C]// Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii: Association for the Advancement of Artificial Intelligence, 2019. |
[28] |
DONAHUE J, ANNE HENDRICKS L, GUADARRAMA S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE Computer Society, 2015. |
[29] |
VAN STEENKISTE S, CHANG M, GREFF K, et al. Relational neural expectation maximization: Unsupervised discovery of objects and their interactions[J]. arXiv: 1802.10353, 2018, https://doi.org/10.48550/arXiv.1802.10353 |
[30] |
KUMAR D, SINGH A, SAMUI P, et al. Forecasting monthly precipitation using sequential modelling[J]. Hydrological Sciences Journal, 2019, 64(6): 690–700, https://doi.org/10.1080/02626667.2019.1595624 |
[31] |
KANG J, WANG H, YUAN F, et al. Prediction of precipitation based on recurrent neural networks in Jingdezhen, Jiangxi Province, China[J]. Atmosphere, 2020, 11(3): 246, https://doi.org/10.3390/atmos11030246 |
[32] |
LI J, YUAN X. Daily streamflow forecasts based on cascade long short-term memory (LSTM) model over the Yangtze River Basin[J]. Water, 2023, 15(6): 1019, https://doi.org/10.3390/w15061019 |
[33] |
MANOKIJ F, VATEEKUL P, SARINNAPAKORN K. Cascading models of CNN and GRU with autoencoder loss for precipitation forecast in Thailand[J]. ECTI Transactions on Computer and Information Technology, 2021, 15(3): 333–346, https://doi.org/10.37936/ecti-cit.2021153.240957 |
[34] |
ZHANG X, DUAN B, HE S, et al. A new precipitation forecast method based on CEEMD-WTD-GRU[J]. Water Supply, 2022, 22(4): 4120–4132, https://doi.org/10.2166/ws.2022.037 |
[35] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems 30. Honolulu: Neural Information Processing Systems Foundation, Inc (NeurIPS), 2017. |
[36] |
KLEIN B, WOLF L, AFEK Y. A dynamic convolutional layer for short range weather prediction[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: Institute of Electrical and Electronics Engineers, 2015. |
[37] |
SHI E, LI Q, GU D, et al. Weather radar echo extrapolation method based on convolutional neural networks[J]. Journal of Computer Applications, 2018, 38(3): 661, https://doi.org/10.11772/j.issn.1001-9081.2017082098 |
[38] |
AYZEL G, HEISTERMANN M, SOROKIN A, et al. All convolutional neural networks for radar-based precipitation nowcasting[J]. Procedia Computer Science, 2019, 150: 186–192, https://doi.org/10.5194/gmd-13-2631-2020 |
[39] |
ZHANG W, HAN L, SUN J, et al. Application of multi-channel 3D-cube successive convolution network for convective storm nowcasting[C]// 2019 IEEE International Conference on Big Data (Big Data). Los Angeles: Institute of Electrical and Electronics Engineers, 2019. |
[40] |
AGRAWAL S, BARRINGTON L, BROMBERG C, et al. Machine learning for precipitation nowcasting from radar images[J]. arXiv: 1912.12132, 2019, https://doi.org/10.48550/arXiv.1912.12132 |
[41] |
FANG W, CHEN Y, XUE Q. Survey on research of RNN-based spatio-temporal sequence prediction algorithms[J]. Journal on Big Data, 2021, 3(3): 97–110, https://doi.org/10.32604/jbd.2021.016993 |
[42] |
ZANG Z, BAO X, LI Y, et al. A modified RNN-based deep learning method for prediction of atmospheric visibility[J]. Remote Sensing, 2023, 15(3): 553, https://doi.org/10.3390/rs15030553 |
[43] |
AKBARI A A, YANG T, HSU K, et al. Short-term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(22): 12, 543–12, 563, https://doi.org/10.1029/2018jd028375 |
[44] |
LIU J, XU L, CHEN N. A spatiotemporal deep learning model ST-LSTM-SA for hourly rainfall forecasting using radar echo images[J]. Journal of Hydrology, 2022, 609: 127748, https://doi.org/10.1016/j.jhydrol.2022.127748 |
[45] |
ASHESH A, CHANG C T, CHEN B F, et al. Accurate and clear quantitative precipitation nowcasting based on a deep learning model with consecutive attention and rain-map discrimination[J]. Artificial Intelligence for the Earth Systems, 2022, 1(3): e210005, https://doi.org/10.1175/AIES-D-21-0005.1 |
[46] |
JING J, LI Q, PENG X, et al. HPRNN: A hierarchical sequence prediction model for long-term weather radar echo extrapolation[C]// ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona: Institute of Electrical and Electronics Engineers, 2020. |
[47] |
SØNDERBY C K, ESPEHOLT L, HEEK J, et al. Metnet: A neural weather model for precipitation forecasting[J]. arXiv: 2003.12140, 2020, https://doi.org/10.48550/arXiv.2003.12140 |
[48] |
SHI X, CHEN Z, WANG H, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting[C]// Advances in Neural Information Processing Systems. Montreal: Neural Information Processing Systems, 2015. |
[49] |
SHI X, GAO Z, LAUSEN L, et al. Deep learning for precipitation nowcasting: A benchmark and a new model [C]// Advances in Neural Information Processing Systems. California: Neural Information Processing Systems, 2017. |
[50] |
KIM S, HONG S, JOH M, et al. Deeprain: ConvLSTM network for precipitation prediction using multichannel radar data[J]. arXiv: 1711.02316, 2017, https://doi.org/10.48550/arXiv.1711.02316 |
[51] |
WANG Y, LONG M, WANG J, et al. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal LSTMs[C]// Advances in Neural Information Processing Systems. California: Neural Information Processing Systems, 2017. |
[52] |
WANG Y, ZHANG J, ZHU H, et al. Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. California: Institute of Electrical and Electronics Engineers, 2019. |
[53] |
WU H, YAO Z, WANG J, et al. MotionRNN: A flexible model for video prediction with spacetime-varying motions [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Montreal: Institute of Electrical and Electronics Engineers, 2021. |
[54] |
WANG Y, GAO Z, LONG M, et al. PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning[C]// International Conference on Machine Learning. Stockholm: Proceedings of Machine Learning Research, 2018: 5123–5132. |
[55] |
RAVURI S, LENC K, WILLSON M, et al. Skilful precipitation nowcasting using deep generative models of radar[J]. Nature, 2021, 597(7878): 672–677, https://doi.org/10.1038/s41586-021-03854-z |
[56] |
TIAN L, LI X, YE Y, et al. A generative adversarial gated recurrent unit model for precipitation nowcasting[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(4): 601–605, https://doi.org/10.1109/LGRS.2019.2926776 |
[57] |
DONG X, ZHAO Z, WANG Y, et al. Motion-guided global–local aggregation transformer network for precipitation nowcasting[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1–16, https://doi.org/10.1109/TGRS.2022.3217639 |
[58] |
YANG Y, MEHRKANOON S. Aa-transunet: Attention augmented transunet for nowcasting tasks[C]// 2022 International Joint Conference on Neural Networks. Padova: Institute of Electrical and Electronics Engineers, 2022. |
[59] |
ZHENG Y, LIAO C. Transformer-based nowcasting model of severe convective weather[C]// Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023). Lianyungang: Jiangsu Ocean University, 2024. |
[60] |
PAN B, WANG L, ZHANG F, et al. Probabilistic diffusion model for stochastic parameterization-A case example of numerical precipitation estimation[Z]. ESS Open Archive, 2023, https://doi.org/10.22541/essoar.170158335.56592781/v1 |
[61] |
PAN B, HAI J, CHEN X, et al. Reliable precipitation nowcasting using probabilistic diffusion model[Z]. ESS Open Archive, 2023, https://doi.org/10.22541/essoar.169945499.97460779/v1 |
[62] |
PRUDDEN R, ADAMS S, KANGIN D, et al. A review of radar-based nowcasting of precipitation and applicable machine learning techniques[Z]. arXiv: 2005.04988, 2020, https://doi.org/10.48550/arXiv.2005.04988 |
[63] |
HU Y, CHEN L, WANG Z, et al. Towards a more realistic and detailed deep-learning-based radar echo extrapolation method[J]. Remote Sensing, 2021, 14(1): 24, https://doi.org/10.3390/rs14010024 |
[64] |
HAN L, LIANG H, CHEN H, et al. Convective precipitation nowcasting using U-Net Model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1–8, https://doi.org/10.1109/TGRS.2021.3100847 |
[65] |
TAN C, GAO Z, LI S, et al. Simvp: Towards simple yet powerful spatiotemporal predictive learning[J]. arXiv: 2211.12509, 2022, https://doi.org/10.48550/arXiv.2211.12509 |
[66] |
CHEN L, DU F, HU Y, et al. SwinRDM: Integrate SwinRNN with diffusion model towards high-resolution and high-quality weather forecasting[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Washington: Association for the Advancement of Artificial Intelligence, 2023. |
[67] |
HU Y, CHEN L, WANG Z, et al. SwinVRNN: A data-driven ensemble forecasting model via learned distribution perturbation[J]. Journal of Advances in Modeling Earth Systems, 2023, 15(2): e2022MS003211, https://doi.org/10.1029/2022MS003211 |
[68] |
ANDRYCHOWICZ M, ESPEHOLT L, LI D, et al. Deep learning for day forecasts from sparse observations[J]. arXiv: 2306.06079, 2023, https://doi.org/10.48550/arXiv.2306.06079 |
[69] |
PATHAK J, SUBRAMANIAN S, HARRINGTON P, et al. Fourcastnet: A global data-driven high-resolution weather model using adaptive Fourier neural operators[J]. arXiv: 2202.11214, 2022, https://doi.org/10.48550/arXiv.2202.11214 |
[70] |
BI K, XIE L, ZHANG H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature, 2023, 619(7970): 533–538, https://doi.org/10.1038/s41586-023-06185-3 |
[71] |
LAM R, SANCHEZ-GONZALEZ A, WILLSON M, et al. Learning skillful medium-range global weather forecasting[J]. Science, 2023, 382(6677): 1416–1421, https://doi.org/10.1126/science.adi2336 |
[72] |
NGUYEN T, BRANDSTETTER J, KAPOOR A, et al. ClimaX: A foundation model for weather and climate[J]. arXiv: 2301.10343, 2023, https://doi.org/10.48550/arXiv.2301.10343 |
[73] |
CHEN K, HAN T, GONG J, et al. FengWu: Pushing the skillful global medium-range weather forecast beyond 10 days lead[J]. arXiv: 2304.02948, 2023, https://doi.org/10.48550/arXiv.2304.02948 |
[74] |
CHEN L, ZHONG X, ZHANG F, et al. FuXi: a cascade machine learning forecasting system for 15-day global weather forecast[J]. npj Climate and Atmospheric Science, 2023, 6(1): 190, https://doi.org/10.1038/s41612-023-00512-1 |
[75] |
DE BÉZENAC E, PAJOT A, GALLINARI P. Deep learning for physical processes: Incorporating prior scientific knowledge[J]. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2019(12): 124009, https://doi.org/10.1088/1742-5468/ab3195 |
[76] |
PAGANINI M, DE OLIVEIRA L, NACHMAN B. CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks[J]. Physical Review D, 2018, 97(1): 014021, https://doi.org/10.1103/PhysRevD.97.014021 |
[77] |
RASP S, PRITCHARD M S, GENTINE P. Deep learning to represent subgrid processes in climate models[J]. Proceedings of the National Academy of Sciences, 2018, 115(39): 9684–9689, https://doi.org/10.1073/pnas.1810286115 |
[78] |
MORRISON H, VAN LIER-WALQUI M, FRIDLIND A M, et al. Confronting the challenge of modeling cloud and precipitation microphysics[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(8): e2019MS001689, https://doi.org/10.1029/2019MS001689 |
[79] |
ZHANG Y, LONG M, CHEN K, et al. Skilful nowcasting of extreme precipitation with NowcastNet[J]. Nature, 2023, 619(7970): 526–532, https://doi.org/10.1038/s41586-023-06184-4 |
[80] |
BEAUCHEMIN S S, BARRON J L. The computation of optical flow[J]. ACM Computing Surveys (CSUR), 1995, 27(3): 433–466, https://doi.org/10.1145/212094.212141 |