气象大数据超短临精准降水机器学习与典型应用

doi:10.19788/j.issn.2096-6369.190108

Abstract

Abstract:

Accurate precipitation nowcasting is essential for agricultural production, hydrological monitoring, flood mitigation, large organizations, and electrical systems. Because of the high uncertainty of weather systems, the performance of precipitation estimation by conventional meteorological methods based on physical models and statistical analysis is unsatisfactory. Determining how to improve the accuracy of precipitation estimation and forecasting in high resolution is challenging. This study proposes the method of terrain-based weighted random forests (TWRF) for radar-based quantitative precipitation estimation (QPE). This method can be regarded as a generalization of random forests via consideration of variations in the vertical profile of reflectivity (VPR) and orographic enhancement of precipitation for complex terrains. The performance was tested within the 45~100 km range of the Z9571 radar in Hangzhou, China during rainfall events in June and July, 2014. The experimental results showed that TWRF is better than conventional methods and random forests, and further indicate that utilization of the entire VPR and terrain-based modeling are effective for radar QPE.

Key words: meteorological big data, quantitative precipitation estimation, random forests, machine learning, weather radar

CLC Number:

Zhang Chenyang, Yang Xuebing, Zhang Wenshen. Accurate Precipitation Nowcasting with Meteorological Big Data: Machine Learning Method and Application[J].Journal of Agricultural Big Data, 2019, 1(1): 78-87.

Figures/Tables 7

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References 31

[1]	S.M. Sumi, M.F. Zaman, H. Hirose . A rainfall forecasting method using machine learning models and its application to the Fukuoka city case[J]. International Journal of Applied Mathematics and Computer Science, 2012,22(4):841-854.
[2]	B. Sivakumar, F.M. Woldemeskel . A network-based analysis of spatial rainfall connections[J]. Environmental Modelling and Software, 2015,69(C):55-62. doi: 10.1016/j.envsoft.2015.02.020
[3]	S. Araghinejad, M. Azmi, M. Kholghi . Application of artificial neural network ensembles in probabilistic hydrological forecasting[J]. Journal of Hydrology, 2011,407:94-104. doi: 10.1016/j.jhydrol.2011.07.011
[4]	A.G. Evsukoff, M. Cataldi, B.S.L.P.D. Lima . A multi-model approach for long-term runoff modeling using rainfall forecasts[J]. Experts Systems with Applications, 2012,39(5):4938-4946. doi: 10.1016/j.eswa.2011.10.023
[5]	俞小鼎, 姚秀萍, 熊廷南 , 等. 多普勒天气雷达原理与业务应用[M]. 北京: 气象出版社, 2006.
	Yu X D, Yao X P, Xiong T N , et al. Principle and Application of Doppler Weather Radar[M]. Beijing: China Meteorological Press, 2006.
[6]	T.W. Harrold, E.J. English, C.A. Nicholass . The accuracy of radar-derived rainfall measurements in hilly terrain[J]. Quarterly Journal of the Royal Meteorological Society, 1974,100(425):331-350. doi: 10.1002/qj.49710042506
[7]	J.S. Marshall , W.M.K. Plamer. The distribution of raindrops with size[J]. Journal of Meteorology, 1948,5(4):165-166.
[8]	J.W. Wilson, E.A. Brandes . Radar measurement of rainfall - a summary[J]. Bulletin of the American Meteorological Society, 1979,60(9):1048-1058. doi: 10.1175/1520-0477(1979)060<1048:RMORS>2.0.CO;2
[9]	W. Hall , M.A. Rico-Ramirez, S. Kramer. Classification and correction of the bright band using an operational C-band polarimetric radar[J]. Journal of Hydrology, 2015,531:248-258. doi: 10.1016/j.jhydrol.2015.06.011
[10]	R. Wexler . Radar detection of a frontal storm 18 June 1946[J]. Journal of Meteorology , 1947 ,4(1): 38-44. doi: 10.1175/1520-0469(1947)0042.0.CO;2
[11]	J.S. Marshall, R.C. Langille, W.M.K. Palmer. Measurement of rainfall by radar[J]. Journal of Meteorology, 1947,4(6):186-192. doi: 10.1175/1520-0469(1947)0042.0.CO;2
[12]	Q. Cao, Y. Hong, Y. Qi , et al. Empirical conversion of the vertical profile of reflectivity from Ku-band to S-band frequency[J]. Journal of Geophysical Research Atmospheres, 2013,118(4):1814-1825. doi: 10.1002/jgrd.50138
[13]	Q. Cao, Y. Wen, Y. Hong , et al. Enhancing quantitative precipitation estimation over the continental United States using a ground-space multi-sensor integration approach[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(7):1305-1309. doi: 10.1109/LGRS.2013.2295768
[14]	J. Zhang, K. Howard, X. Xu, et al. A warm season radar QPE algorithm using adaptive Z-R relationships [C]. World Environmental and Water Resources Congress, 2008, 1-10.
[15]	H. Seyyedi, E.N. Anagnostou, P.E. Kirstetter , et al. Incorporating surface soil moisture information in error modeling of TRMM passive microwave rainfall[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(10):6226-6240. doi: 10.1109/TGRS.2013.2295795
[16]	肖现, 廖菲, 肖辉 , 等. 北京对流性降水的雨滴尺寸分布瞬时特征与雷达降水的关系[J]. 热带气象学报, 2010,26(4):445-451.
	Xiao X, Liao F, Xiao H , et al. Systematic Variation of Drop Size in Convective if Beijing and Radar-Rainfall Relations[J]. Journal of Tropical Meteorology, 2010,26(4):445-451.
[17]	肖艳娇, 万玉发, 王珏 , 等. 改进的C波段天气雷达定量降水估计算法研究[J]. 暴雨灾害, 2011,30(4):321-327.
	Xiao Y J, Wan Y F, Wang J , et al. Study of an Improved Modified C-Band Radar Quantitative Precipitation Estimation Algorithm[J]. Torrential Rain and Disasters, 2011,30(4):321-327.
[18]	高晓荣, 梁建茵, 李春晖 , 等 . 多平台(雷达、卫星、雨量计)降水信息的融合技术初探[J]. 高原气象, 2013,32(2):549-555.
	Gao X R, Liang J Y, LI C H , et al. Preliminary Studies on Merged Techniques Based on Precipitation Information from Multiplatform(Radar,Satellite and Rain Gauge)[J]. Plateau Meteorology, 2013,32(2):549-555.
[19]	龚雪鹏, 徐晓, 聂祥 , 等. 毕节CINRAD/CD雷达定量估测降水Z-I关系的研究[J]. 科技风, 2017,1:77-78.
	Gong X P, Xu X, Nie X , et al. Research on the Quantitative Estimation of Precipitation Z-I relationship by CINRAD/CD Radar in Bijie[J]. Technology Wind, 2017,1:77-78.
[20]	R. Xiao, V. Chandrasekar . Development of a neural network based algorithm for rainfall estimation from radar observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(1):160-171. doi: 10.1109/36.551944
[21]	W. Hong . Rainfall forecasting by technological machine learning models[J]. Applied Mathematics and Computation, 2008,200(1):41-57. doi: 10.1016/j.amc.2007.10.046
[22]	A. Kusiak, X. Wei, A.P. Verma , et al. Modeling and prediction of rainfall using radar reflectivity data: a data-mining approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(4):2337-2342. doi: 10.1109/TGRS.2012.2210429
[23]	X. Shi, Z. Chen, H. Wang, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting [C]. International Conference on Neural Information Processing Systems, 2015, 802-810.
[24]	X. Shi, Z. Gao, L. Lausen, et al. Deep learning for precipitation nowcasting: a benchmark and a new model [C]. International Conference on Neural Information Processing Systems, 2017, 5617-5627.
[25]	L. Breiman . Random forests[J]. Machine Learning, 2001,45(1):5-32. doi: 10.1023/A:1010933404324
[26]	M. Fernàndez-Delgado, E. Cernadas, S. Barro , et al. Do we need hundreds of classifiers to solve real world classification problems?[J]. Journal of Machine Learning Research, 2014,15(1):3133-3181.
[27]	B. Xu, J.Z. Huang, G. Williams , et al. Classifying very high-dimensional data with random forests built from small subspaces[J]. International Journal of Data Warehousing and Mining, 2012,8(2):44-63. doi: 10.4018/jdwm.2012040103
[28]	E. Richard, N. Chaumerliac, J.F. Mahfouf , et al. Numerical simulation of orographic enhancement of rain with a mesoscale model[J]. Journal of Applied Meteorology, 1987,26(6):661-669.
[29]	V. Lakshmanan, A. Fritz, T. Smith , et al. An automated technique to quality control radar reflectivity data[J]. Journal of Applied Meteorology and Climatology, 2007,46(3):288-305. doi: 10.1175/JAM2460.1
[30]	P.P. Mapiam, N. Sriwongsitanon . Climatological Z-R relationship for radar rainfall estimation in the upper Ping river basin[J]. ScienceAsia, 2008,34(2):215-222.
[31]	F. Fabry, A. Bellon, M.R. Duncan , et al. High resolution rainfall measurements by radar for very small basins: the sampling problem reexamined[J]. Journal of Hydrology, 1994,161:415-428. doi: 10.1016/0022-1694(94)90138-4

方法	RMSE (mm h^-1)	MAE (mm h^-1)	CC
Z-R关系	2.318	1.132	0.756
RF	2.101	1.040	0.801
WRF	2.046	1.022	0.812
TWRF	1.991	0.997	0.823

Accurate Precipitation Nowcasting with Meteorological Big Data: Machine Learning Method and Application

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