Abstract
The spatial distribution and depth of precipitation are the main driving factors for the formation of flood disasters. Precipitation nowcasting plays a crucial role in rainstorm warning, flood mitigation and water resources management. However, high spatiotemporal resolution nowcasting is very challenging owing to the uncertain dynamics and chaos, especially at a small-scale region. In recent years, deep learning approaches were applied in precipitation nowcasting and achieved good performance in learning spatiotemporal features. In this paper, ConvLSTM model and sequences of radar reflectivity maps were used to forecast the future sequence of reflectivity maps with up to 2 h lead time in Liulin watershed with a small area of 57.4 km2. Dynamic hierarchical Z–I relationship was employed to calculate the forecasting precipitation and the forecasted spatiotemporal features were compared to the observed. The results indicated that the model can provide a well performance for the reflectivity above 10 dBZ with 0.70 of CSI for 30 min nowcasting and 0.57 for 2 h nowcasting, but was not good at forecasting the reflectivity above 30 dBZ with 0.38 of mean CSI for 30 min nowcasting and 0.12 for 2 h nowcasting, which have a decrease of 45.7% and 78.9%, respectively. The forecasted precipitation could truly show the details of precipitation spatial distribution and provide the accuracy of forecasting area with 49.2% for 30 min nowcasting. The satisfied areal precipitation depth could be offered basically with 26.3% of Bias for 30 min nowcasting in Liulin watershed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data during the current study are not publicly available but are available from the corresponding author on reasonable request.
Code availability
The code used for the analysis described in the paper is available on request.
References
Bao H, Cao Y, Cao S, Wang M (2021) Flood forecasting of small and medium-sized rivers based on short-term nowcasting and ensemble precipitation forecasts. J Hohai Univ (nat Sci) 49(3):197–203. https://doi.org/10.3876/j.issn.1000-1980.2021.03.001
Bonnet SM, Evsukoff A, Morales RCA (2011) Precipitation nowcasting with weather radar images and deep learning in São Paulo, Brasil. Atmosphere 11(11):1157–1157. https://doi.org/10.3390/atmos11111157
Browning KA, Collier CG, Larke PR, Menmuir P, Monk GA, Owens RG (1982) On the forecasting of frontal rain using a weather radar network. Mon Weather Rev 110:534–552. https://doi.org/10.1175/1520-0493
Chang L, Deng G (2021) Short term load forecasting of science and technology park based on integration of LightGBM and LSTM. Hunan Electr Pow 41(6):31–35. https://doi.org/10.3969/j.issn.1008-0198.2021.06.007
Chen L, Cao Y, Ma L, Zhang J (2020) A deep learning-based methodology for precipitation nowcasting with radar. Earth Space Sci 7(2):e2019EA000812. https://doi.org/10.1029/2019EA000812
Crane RK (1979) Automatic cell detection and tracking. IEEE Trans Geosci Electron 17(4):250–262. https://doi.org/10.1109/TGE.1979.294654
Fang W, Shen L, Victor SS, Xue Q (2022) A novel method for precipitation nowcasting based on ST-LSTM. Comput Mater & Continua 72(3):4867–4877. https://doi.org/10.32604/cmc.2022.027197
Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17:185–203. https://doi.org/10.1016/0004-3702(81)90024-2
Hosseinzadehtalaei P, Ishadi NK, Tabari H, Willems P (2021) Climate change impact assessment on pluvial flooding using a distribution-based bias correction of regional climate model simulations. J Hydrol 598:126239. https://doi.org/10.1016/j.jhydrol.2021.126239
Huang Q, Mai X, Li L, Tang J, Tang F (2021) Forecast of short-term precipitation in Guangxi based on ConvLSTM. J Meteorol Res Appl 42(04):44–49. https://doi.org/10.19849/j.cnki.CN45-1356/P.2021.4.08
Kang L (2017) A study on dynamic correction for radar-derived quantitation precipitation estimation based on the optimal Z–I relationship. Dissertation, Lanzhou University
Kumar A, Islam T, Sekimoto Y, Mattmann C, Wilson B (2020) Convcast: an embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data. PLoS ONE 15(3):e0230114. https://doi.org/10.1371/journal.pone.0230114
Lebedev V, Ivashkin V, Rudenko I, Ganshin A, Molchanov A, Ovcharenko S, Grokhovetskiy R, Bushmarinov I, Solomentsev D (2019) Precipitation nowcasting with satellite imagery. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. p 2680–2688. https://doi.org/10.1145/3292500.3330762
Li L (2020) The technique and application research on precipitation nowcasting based radar data. Dissertation, Chengdu University of Technology
Li L, Guo J, Xu X, Feng T, Yang L (2017) Analysis of application and effect of refined numerical precipitation forecast in Zhexi hydropower plant. Hunan Electr Pow 37(6):66–69. https://doi.org/10.3969/j.issn.1008-0198.2017.06.018
Li Y (2019) Research on precipitation nowcasting method based on wind profile radar data. Dissertation, Chengdu University of Information Technology
Li Y (2021) Quantitative precipitation nowcasting based on deep learning and weather radar data. Dissertation, Qinghai University
Li Y, Li Q, Wei J, Qiao Z, Shen W (2021) Radar echo extrapolation of weather based on ConvLSTM. J Qinghai Univ 39(1):93–100. https://doi.org/10.13901/j.cnki.qhwxxbzk.2021.01.014
Liang Z, Chen S (2020) Accuracy evaluation of nowcasting in south China based on deep learning and radar observation. J Meteorol Res Appl 41(1):41–47. https://doi.org/10.19849/j.cnki.CN45-1356/P.2020.1.09
Liu J, Qiu Q, Li C, Jiao Y, Wang W, Yu F (2020a) Advances of precipitation nowcasting and its application in hydrological forecasting. Adv Water Sci 31(1):129–142. https://doi.org/10.14042/j.cnki.32.1309.2020.01.014
Liu W, Wang Y, Zhong D, Xie S, Xu J (2022) ConvLSTM network-based rainfall nowcasting method with combined reflectance and radar-retrieved wind field as inputs. Atmosphere 13:411. https://doi.org/10.3390/atmos13030411
Liu Y, Zheng C, Ma C (2020b) Validity evaluation of the method for quantitatively estimating precipitation by applying dynamic Z–R relation to rainstorm cases. J Shaanxi Meteorol 5:6–10
Luo C, Zhao X, Sun Y, Li X, Ye Y (2022) PredRANN: the spatiotemporal attention convolution recurrent neural network for precipitation nowcasting. Knowl-Based Syst 239:107900. https://doi.org/10.1016/j.knosys.2021.107900
Ma J, Cui X, Jiang N (2022) Modelling the Z–R relationship of precipitation nowcasting based on deep learning. Comput, Mater Continua 72(1):1939–1949. https://doi.org/10.32604/cmc.2022.025206
Mi J, Tian J, Chu Z, Liu R, Xue H (2022) Study on flood forecasting of small and media size watersheds by coupling weather radar quantitative precipitation estimate and quantitative precipitation forecast. J North China Univ Water Resour Electr Pow (nat Sci Ed) 43(3):11–18. https://doi.org/10.19760/j.ncwu.zk.2022030
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) Skillful precipitation nowcasting using deep generative models of radar. Nature 597:672–677. https://doi.org/10.1038/s41586-021-03854-z
Rinehart RE, Garvey ET (1978) Three-dimensional storm motion detection by conventional weather radar. Nature 273:287–289. https://doi.org/10.1038/273287a0
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. Adv Neural Inf Process Syst 28:802–810. https://doi.org/10.48550/arXiv.1506.04214
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. Adv Neural Inf Process Syst 30:5617–5627
Sønderby CK, 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 2003:12140. https://doi.org/10.48550/arXiv.2003.12140
Sun X (2020) Research on precipitation nowcasting based on echo extrapolation algorithm in China radar. Dissertation, South China University of Technology
Tian J, Liu J, Yan D, Li C, Chu Z, Yu F (2017) An assimilation test of doppler radar reflectivity and radial velocity from different height layers in improving the WRF rainfall forecasts. Atmos Res 198:132–144. https://doi.org/10.1016/j.atmosres.2017.08.004
Tuyen DN, Tran TM, Le X-H, Tung NT, Chau TK, Hai PV, Gerogiannix VC, Son LH (2022) RainPredRNN: a new approach for precipitation nowcasting with weather radar echo images based on deep learning. Axioms 11(3):107. https://doi.org/10.3390/axioms11030107
Wang W (2020) Research on the application of rainfall estimation technology based on radar in flood forecast of biliuhe river watershed. Dissertation, Dalian University of Technology
Wang Y, Feng Y, Cai J, Hu S (2011) An approach for radar quantitative precipitation estimate based on categorical Z–I relations. J Trop Meteorol 27:601–608. https://doi.org/10.3969/j.issn.1004-4965.2011.04.018
Wang Y, Long M, Wang J, Gao Z, Yu PS (2017) PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs. Adv in Neural Inf Process Syst 30:879–888
Xun L, Yao Q, Ji F, Li T, Zhang J, Miao K, Yan Q (2022) PPNet: a more effective method of precipitation prediction. Meteorol Appl 29:e2081. https://doi.org/10.1002/met.2081
Yu W, Nakakita E, Kim S, Yanmaguchi K (2015) Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall. J Hydrol 531:494–507. https://doi.org/10.1016/j.jhydrol.2015.04.055
Zbyněk S, Jan M, Lukáš P, Vojtěch B (2017) Probabilistic precipitation nowcasting based on an extrapolation of radar reflectivity and an ensemble approach. Atmos Res 194:245–257. https://doi.org/10.1016/j.atmosres.2017.05.003
Zhang J, Huang Y, Liu C (2021) Precipitation nowcasting based on convolutional gated recurrent unit and weather radar image. Comput Digit Eng 49(8):1538–1542. https://doi.org/10.3969/j.issn.1672-9722.2021.08.007
Acknowledgements
This study was supported by the Natural Science Foundation of Tianjin (20JCQNJC01960) and National Natural Science Foundation of China (52279022, 52079086). We would acknowledge the Hebei Xingtai Meteorological Bureau for kindly providing the radar observations. We would also acknowledge the Hebei Xingtai Hydrological Survey and Research Center for the observed precipitation at five rain gauges.
Funding
This study was funded by the Natural Science Foundation of Tianjin (20JCQNJC01960) and National Natural Science Foundation of China (52279022, 52079086).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by TZ, ZL, CW and JL. Model setup was completed by JL. The first draft of the manuscript was written by YS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, J., Shi, Y., Zhang, T. et al. Radar precipitation nowcasting based on ConvLSTM model in a small watershed in north China. Nat Hazards 120, 63–85 (2024). https://doi.org/10.1007/s11069-023-06193-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-06193-6