A Particle Swarm Optimization-neural Network Ensemble Prediction Model for Persistent Freezing Rain and Snow Storm in Southern China

Lu Hong; Zhai Panmao; Qin Weijian; Jin Long; Xie Min; Qian Xi; Zhao Huasheng

doi:10.11898/1001-7313.20150501

Vol.26, NO.5, 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2015 > 26(5): 513-524

Lu Hong, Zhai Panmao, Qin Weijian, et al. A particle swarm optimization-neural network ensemble prediction model for persistent freezing rain and snow storm in Southern China. J Appl Meteor Sci, 2015, 26(5): 513-524. DOI: 10.11898/1001-7313.20150501.

Citation:

Lu Hong, Zhai Panmao, Qin Weijian, et al. A particle swarm optimization-neural network ensemble prediction model for persistent freezing rain and snow storm in Southern China. J Appl Meteor Sci, 2015, 26(5): 513-524. DOI: 10.11898/1001-7313.20150501.

Citation:

Lu Hong, Zhai Panmao, Qin Weijian, et al. A particle swarm optimization-neural network ensemble prediction model for persistent freezing rain and snow storm in Southern China. J Appl Meteor Sci, 2015, 26(5): 513-524. DOI: 10.11898/1001-7313.20150501.

PDF( 729 KB)

A Particle Swarm Optimization-neural Network Ensemble Prediction Model for Persistent Freezing Rain and Snow Storm in Southern China

DOI: 10.11898/1001-7313.20150501

1.
Guangxi Climate Center, Nanning 530022
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Wujiang District Meteorological Service of Jiangsu Province, Suzhou 215200

Received Date: 2014-12-04
Rev Recd Date: 2015-06-09
Publish Date: 2015-09-30

Abstract

Abstract

Based on daily minimum temperature, maximum temperature and precipitation data of 756 stations in China, National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data during 1951-2013 and NCEP 24 h forecast data, a nonlinear statistical ensemble prediction model based on the particle swarm optimization-neural network (PSONN-EPM) is developed for predicting and verifying the regional persistent freezing rain and snow storm process in southern China by analyzing and extracting significant predictors. Results show that model performance can be effectively improved when dividing low-temperature processes into the general process and severe process which are constructed based on cold extents, humidity and influence ranges of the freezing rain and snow storm processes. In 10-day independent forecast test, the average relative errors for the general process and the severe process are 2.04 and 0.6 using stepwise regression equation forecast method, while those are 1.33 and 0.30 by using PSONN-EPM technique. It means forecast errors are reduced by 0.71 and 0.3 as compared with the stepwise regression method. In addition, the predication result for the severe freezing rain and snow storm process is better than that for the general process. The PSONN-EPM integrates predictions of multiple ensemble members, thus the prediction accuracy and stability are higher than those of the traditional linear regression method. Furthermore, such method does not contain any tunable parameters, and is applicable for practical operational weather prediction.
- particle swarm optimization algorithm;
- neural network;
- persistence;
- freezing rain and snow storm;
- ensemble prediction

FullText(HTML)

References(29)

Figures and Tables

图 1 冷湿指数与850 hPa温度相关分布

(阴影表示达到0.001显著性水平)

Fig. 1 Correlation of PT value to 850 hPa temperature

(the shaded denotes passing the test of 0.001 level)

DownLoad: Full-Size Img PowerPoint

Fig. 2 Fitting values of general (a) and severe (b) processes by the particle swarm optimization-neural network ensemble prediction model

DownLoad: Full-Size Img PowerPoint

Table 1 Cold rain and snow events in the study area during 1951-2013

开始日期	结束日期	持续日数/d	影响站数	过程日最大冷湿指数
1954-12-07	1954-12-15	9	3	13.05
1954-12-26	1955-01-10	16	30	70.42
1956-01-06	1956-01-12	7	6	20.18
1956-01-20	1956-01-26	7	4	9.08
1957-01-12	1957-01-16	5	4	20.86
1957-02-04	1957-02-16	13	10	18.77
1958-01-15	1958-01-19	5	4	8.83
1958-01-29	1958-02-04	7	5	12.91
1960-01-23	1960-01-28	6	3	5.68
1961-01-11	1961-01-16	6	3	7.43
1962-01-15	1962-01-29	15	3	8.34
1964-01-23	1964-02-04	13	8	22.9
1964-02-15	1964-02-28	14	46	112.27
1966-12-25	1967-01-12	19	11	17.74
1967-02-10	1967-02-15	6	4	6.56
1968-02-01	1968-02-10	10	11	21.06
1969-01-11	1969-01-17	7	17	26.24
1969-01-28	1969-02-09	13	23	80.65
1969-02-14	1969-02-28	15	4	6.19
1971-01-15	1971-02-05	12	16	63.73
1972-12-29	1973-01-06	9	6	13.25
1972-02-03	1972-02-11	9	51	138.71
1974-01-23	1974-02-12	21	29	65.51
1975-12-08	1975-12-15	8	22	73.48
1976-12-26	1977-01-17	23	18	36.67
1977-01-26	1977-02-04	10	21	64.39
1980-01-29	1980-02-13	16	38	79.62
1981-01-25	1981-01-31	7	9	17.59
1982-02-06	1982-02-15	10	7	37.71
1983-12-22	1984-01-02	12	7	51.72
1983-01-08	1983-01-23	16	12	31.13
1984-12-18	1984-12-31	14	13	31.27
1984-01-16	1984-02-11	27	28	50.74
1989-01-11	1989-01-16	6	9	35.09
1989-01-29	1989-02-09	12	3	6.87
1990-01-30	1990-02-04	6	7	11.65
1991-12-25	1991-12-31	7	4	24.31
1993-01-13	1993-01-24	12	14	23.36
1996-02-17	1996-02-26	10	32	81.31
1998-01-18	1998-01-25	8	3	13.11
2000-01-27	2000-02-05	10	6	25.30
2004-02-03	2004-02-08	6	3	7.63
2008-01-13	2008-02-15	34	71	264.24
2010-02-16	2010-02-20	5	3	10.47
2011-01-02	2011-02-01	31	24	118.86
2012-01-21	2012-01-27	7	6	14.64
2013-01-02	2013-01-13	12	5	7.28

DownLoad: Download CSV

Table 2 Predictors used in general process forecasting models (F=3)

因子序号	因子名称	相关系数
X₃	850 hPa江南区域气温	-0.37
X₅	非洲北部上空850 hPa与700 hPa温度差	-0.37
X₈	200 hPa印度半岛西北部与青藏高原区域高度差	0.30
X₉	500 hPa北太平洋北部与南部的高度差	0.25
X₁₂	700 hPa菲律宾北部区域的湿度	-0.26
X₁₆	500 hPa印度半岛与内蒙古区域的水平风速差	0.34
X₁₇	700 hPa印度半岛西北部区域水平风速	0.27
X₁₈	850 hPa太平洋夏威夷和库克群岛区域水平风速差	0.34
X₂₁	850 hPa长江中上游与越南北部区域垂直风速差	0.34
X₂₂	500 hPa孟加拉湾北部与蒙古区域的风速差	0.40

DownLoad: Download CSV

Table 3 Predictors used in severe process forecasting models (F=3)

因子序号	因子名称	相关系数
X₁	850 hPa与700 hPa江南区域温度差	-0.49
X₆	850 hPa贝加尔湖区域温度	0.35
X₉	500 hPa贝加尔湖到我国东北区域高度	0.42
X₁₂	700 hPa长江中下游区域湿度	0.43
X₁₃	700 hPa孟加拉湾区域上空湿度	0.43
X₁₄	850 hPa孟加拉湾到越南北部上空湿度	0.58
X₁₅	850 hPa赤道索马里上空湿度	-0.62
X₁₉	850 hPa半太平洋北部区域与南部区域水平风速差	0.36
X₂₃	700 hPa鄂霍次克海与蒙古高原区域垂直风速差	0.41
X₂₅	850 hPa江南与东海区域垂直风速差	0.58

DownLoad: Download CSV

Table 4 Statistics of predicted values of independent samples from two different processes using the particle swarm optimization-neural network ensemble prediction model

一般过程 (F=3)				严重过程 (F=3)
实况值	预报值	误差	相对误差/%	实况值	预报值	误差	相对误差/%
6.39	5.44	-0.95	15	24.06	32.70	8.64	36
5.00	4.82	-0.18	4	20.41	29.46	9.05	44
7.28	8.52	1.24	17	26.09	36.19	10.10	39
5.25	11.39	6.14	117	21.28	22.94	1.66	8
4.82	28.45	23.63	490	65.69	41.38	-24.31	37
5.80	8.50	2.70	47	47.37	61.42	14.05	30
3.81	6.39	2.58	68	118.86	49.22	-69.64	59
2.49	6.68	4.19	168	62.95	60.33	-2.62	4
1.08	4.29	3.21	297	27.82	27.83	0.01	1
1.35	2.84	1.49	111	15.86	22.14	6.28	40

DownLoad: Download CSV

Table 5 Statistics of predicted values of independent samples from two different processes using stepwise regression equation

一般过程 (F=3)				严重过程 (F=3)
实况值	预报值	误差	相对误差/%	实况值	预报值	误差	相对误差/%
6.39	6.84	0.45	7	24.06	45.09	21.03	87
5.00	5.98	0.98	20	20.41	31.55	11.14	55
7.28	8.49	1.21	17	26.09	38.47	12.38	47
5.25	11.43	6.18	118	21.28	9.50	-11.78	55
4.82	16.49	11.67	242	65.69	57.08	-8.61	13
5.80	11.35	5.55	96	47.37	63.77	16.40	35
3.81	10.19	6.38	167	118.86	52.22	-66.64	56
2.49	10.80	8.31	334	62.95	56.09	-6.86	11
1.08	8.10	7.02	650	27.82	-0.32	-28.14	101
1.35	6.55	5.20	385	15.86	-6.04	-21.90	138

DownLoad: Download CSV

Table 6 Statistics of predicted errors of independent samples from general processes using stepwise regression method and neural network method with different F values (unit:%)

F=2		F=3		F=4
逐步回归	神经网络	逐步回归	神经网络	逐步回归	神经网络
37	20	7	15	0	18
48	25	20	4	21	18
24	9	17	17	26	14
127	91	118	117	125	98
250	323	242	490	246	723
116	51	96	47	89	29
184	76	167	68	148	41
390	285	334	168	291	132
678	386	650	297	573	241
532	279	385	111	328	151

DownLoad: Download CSV

Table 7 Statistics of predicted errors of independent samples from severe processes using stepwise regression method and neural network method with different F values (unit:%)

F=2		F=3		F=4
逐步回归	神经网络	逐步回归	神经网络	逐步回归	神经网络
53	20	87	36	107	63
6	92	55	44	114	46
56	25	47	39	89	44
86	26	55	8	20	30
2	31	13	37	3	33
40	30	35	30	29	28
47	54	56	59	51	61
0	29	11	4	10	14
109	15	101	1	57	7
188	59	138	40	50	67

DownLoad: Download CSV

Table 8 Statistics of predicted values of independent samples from all the samples using the particle swarm optimization-neural network ensemble prediction model

实况值	F=2			F=3			F=4
实况值	预报值	误差	相对误差/%	预报值	误差	相对误差/%	预报值	误差	相对误差/%
6.39	21.35	14.96	234	19.45	13.06	204	26.04	19.65	308
5.00	10.75	5.75	115	10.87	5.87	117	17.86	12.86	257
7.28	14.99	7.71	106	14.21	6.93	95	19.06	11.78	162
5.25	15.28	10.03	191	17.59	12.34	235	20.72	15.47	295
4.82	16.44	11.62	241	14.72	9.90	205	21.08	16.26	337
5.80	13.24	7.44	128	11.39	5.59	96	22.28	16.48	284
3.81	3.89	0.08	2	6.83	3.02	79	7.80	3.99	105
2.49	2.88	0.39	16	3.92	1.43	57	0.84	-1.65	66
1.08	1.11	0.03	3	1.80	0.72	67	7.29	6.21	575
1.35	0.74	-0.61	45	1.68	0.33	25	3.09	1.74	129

DownLoad: Download CSV

References

[1]	陶诗言, 卫捷.2008年1月中国南方严重冰雪灾害过程分析.气候与环境研究, 2008, 13(4):337-351. http://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200804001.htm
[2]	丁一汇, 王遵娅, 宋亚芳, 等.中国南方2008年1月罕见低温雨雪冰冻灾害发生的原因及其与气候变暖的关系.气象学报, 2008, 66(5):808-825. doi: 10.11676/qxxb2008.074
[3]	李崇银, 杨辉, 顾薇.中国南方雨雪冰冻异常天气原因的分析.气候与环境研究, 2008, 13(2):113-122. http://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200802000.htm
[4]	赵思雄, 孙建华.2008年初南方雨雪冰冻天气的环流场与多尺度特征.气候与环境研究, 2008, 13(4):351-367. http://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200804002.htm
[5]	彭艳, 王钊, 刘安麟, 等.2008年1月中国南部低温雨雪冰冻天气特征及其与东亚大气环流异常探讨.大气科学学报, 2010, 33(5):634-640. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201005016.htm
[6]	钱维宏, 张宗婕.南方持续低温冻雨事件预测的前期信号.地球物理学报, 2012, 55(5):1501-1512. doi: 10.6038/j.issn.0001-5733.2012.05.007
[7]	张宗婕, 钱维宏.中国冬半年区域持续性低温事件的前期信号.大气科学, 2012, 36(6):1269-1279. doi: 10.3878/j.issn.1006-9895.2012.11227
[8]	金龙, 吴建生, 林开平, 等.基于遗传算法的神经网络短期气候预测模型.高原气象, 2000, 24(6):981-987. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX200312005034.htm
[9]	Jin L, Yao C, Huang X Y.A nonlinear artificial intelligence ensemble prediction model for typhoon intensity.Mon Wea Rev, 2008, 136:4541-4554. doi: 10.1175/2008MWR2269.1
[10]	Yao C, Jin L, Zhao H S.Ensemble prediction olmon-soon index with a genetic neural network model.Acta Meteor Sinica, 2009, 23(6):701-712. https://www.researchgate.net/profile/Yuehui_Chen/publication/222432560_Flexible_neural_trees_ensemble_for_stock_index_modeling/links/0fcfd50a70809e1d25000000.pdf?inViewer=true&disableCoverPage=true&origin=publication_detail
[11]	陆虹, 金龙, 缪启龙, 等.影响广西热带气旋年频数的神经网络预测模型.南京气象学院学报, 2003, 26(1):56-62. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX200301007.htm
[12]	吴振玲, 潘璇, 董昊, 等.天津市多模式气温集成预报方法.应用气象学报, 2014, 25(3):293-301. doi: 10.11898/1001-7313.20140305
[13]	张振华, 苗春生, 曾智华, 等.一种人工神经网络云分类方法的改进与应用.应用气象学报, 2012, 23(3):355-363. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120312&flag=1
[14]	闵晶晶, 孙景荣, 刘还珠, 等.一种改进的BP算法及在降水预报中的应用.应用气象学报, 2010, 21(1):55-62. doi: 10.11898/1001-7313.20100107
[15]	黄小刚, 费建芳, 陈佩燕.利用神经网络方法建立热带气旋强度预报模型.应用气象学报, 2009, 20(6):699-705. doi: 10.11898/1001-7313.20090607
[16]	Kennedy J, Eberhart R C.Particle Swarm Optimization//Pro IEEE International Conference on Neural Networks.1995.
[17]	Wu J S, Jin L.Study on the meteorological prediction model using the learning algorithm of neural ensemble based on PSO algorithms.Journal of Tropical Meteorology, 2009, 15(1):83-88. http://en.cnki.com.cn/Article_en/CJFDTOTAL-RDQX200806013.htm
[18]	Machado T R, Lopes H S.A Hybrid Particle Swarm Optimization Model for the Traveling Salesman Problem//Adaptive and Natural Computing Algorithms.New York:Springer, 2005:255-258.
[19]	Jin L, Zhu J S, Huang Y, et al.A nonlinear statistical ensemble model for short-range rainfall prediction.Theoretical and Applied Climatology, 2015, 119(3-4):791-807. doi: 10.1007/s00704-014-1161-8
[20]	Jin L, Huang Y, Zhao H S.Ensemble Prediction of Monthly Mean Rainfall with a Particle Swarm Optimization-Neural Network Model.13th IEEE International Conference on Information Reuse and Integration, 2012:287-294.
[21]	龚志强, 王晓娟, 崔冬林, 等.区域性极端低温事件的识别及其变化特征.应用气象学报, 2012, 23(2):195-204. doi: 10.11898/1001-7313.20120208
[22]	韩荣青, 陈丽娟, 李维京, 等.2-5月我国低温连阴雨和南方冷害时空特征.应用气象学报, 2009, 20(3):312-320. doi: 10.11898/1001-7313.20090307
[23]	郑维忠, 倪允琪.热带和中纬太平洋海温异常对东北夏季低温冷害影响的诊断分析研究.应用气象学报, 1999, 10(4):394-401. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=199904105&flag=1
[24]	Zhang H, Qin J, Li Y.Climatic background of cold and wet winter in southern China: Part Ⅰ observational analysis.Climate Dynamics, 2011, 37(11-12):2335-2354. doi: 10.1007/s00382-011-1022-4
[25]	Qian X, Miao Q L, Zhai P M, et al.Cold-wet spells in mainland China during 1951-2011.Nat Hazards, 2014, 70:975-994. doi: 10.1007/s11069-013-0856-y
[26]	王绍武.中国冷冬的气候特征.气候变化研究进展, 2008, 4(2):68-72. http://www.cnki.com.cn/Article/CJFDTOTAL-QHBH200802005.htm
[27]	王东海, 柳崇健, 刘英, 等.2008年1月中国南方低温雨雪冰冻天气特征及其天气动力学成因的初步分析.气象学报, 2008, 66(3):405-422. doi: 10.11676/qxxb2008.038
[28]	杨贵名, 孔期, 毛冬艳, 等.2008年初"低温雨雪冰冻"灾害天气的持续性原因分析.气象学报, 2008, 66(5):638-648. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200805016.htm
[29]	纪忠萍, 高晓容, 谷德军, 等.广东低温阴雨的低频振荡及环流特征.应用气象学报, 2013, 24(1):32-42. doi: 10.11898/1001-7313.20130104