Prediction of Meteorological Elements Based on Nonlinear Support Vector Machine Regression Method

Wang Zaiwen; Zheng Zuofang; Chen Min; Gao Hua

Vol.23, NO.5, 2012

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(5): 562-570

Wang Zaiwen, Zheng Zuofang, Chen Min, et al. Prediction of meteorological elements based on nonlinear support vector machine regression method. J Appl Meteor Sci, 2012, 23(5): 562-570.

Citation:

Wang Zaiwen, Zheng Zuofang, Chen Min, et al. Prediction of meteorological elements based on nonlinear support vector machine regression method. J Appl Meteor Sci, 2012, 23(5): 562-570.

Wang Zaiwen, Zheng Zuofang, Chen Min, et al. Prediction of meteorological elements based on nonlinear support vector machine regression method. J Appl Meteor Sci, 2012, 23(5): 562-570.

Citation:

Wang Zaiwen, Zheng Zuofang, Chen Min, et al. Prediction of meteorological elements based on nonlinear support vector machine regression method. J Appl Meteor Sci, 2012, 23(5): 562-570.

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Prediction of Meteorological Elements Based on Nonlinear Support Vector Machine Regression Method

Institute of Urban Meteorology, CMA, Beijing 100089

Received Date: 2012-02-01
Rev Recd Date: 2012-07-02
Publish Date: 2012-10-31

Abstract

Abstract

Nonlinear regression method based on basic support vector machine is introduced, which is able to solve nonlinear problems. The cross validation in this method makes it able to optimize the kernel function parameters.Therefore, in numerical weather prediction interpretation, nonlinear support vector machine regression technique is better than multi-variant MOS regression method when the linear relationship between the predictor and certain elements, such as wind and specific humidity, is not clear. The numerical prediction products of operational meso-scale model (MM5V3) in Beijing Meteorological Service and observations are used to make 6—48 h interpretation products with 3-hour interval of meteorological elements of 15 venues stations in Beijing. The comparison of interpretation products and MM5V3 forecast indicates that the root mean square error for 2 m temperature, 10 m wind u component, 10 m wind v component and 2 m specific humidity reduces by 12.1%, 43.3%, 53.4% and 38.2%. Compared with the prediction results of MOS, 2 m temperature, 10 m wind u compoent, v component prediction results of SVM are slightly better than those of MOS, and 2 m specific humidity prediction result of SVM is better than that of MOS.Defining the forecast with deviations no more than 2℃ as accurate for 2 m temperature, the forecast accuracy of SVM-release, MOS-release, MM5V3 model and T213 model are 66.5%, 62.2%, 58.8% and 2.5%, respectively. Forecast accuracy of 10 m wind u, v components are defined as the percentage of forecast with absolute deviations within 1 m/s, thus the forecast accuracy of SVM-release are 77.6% and 76.7%, forecast accuracy of MOS-release are 75.8% and 73.7%, forecast accuracy of MM5V3 model are 54.5% and 41.1%, and forecast accuracy of T213 model are 46.9% and 34.9%.Forecast accuracy of 2 m specific humidity is defined as the percentage of forecast with absolute deviations within 2 g·kg^-1, thus the forecast accuracy of SVM-release, MOS-release and MM5V3 model are 84.9%, 67.8% and 61.7%, respectively. It shows that nonlinear support vector machine regression method is good at solving nonlinear dependence between meteorological elements and predictors, and performs better than MOS.
- Support Vector Machine (SVM);
- MOS;
- interpretation;
- meteorological element forecast

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References(16)

Figures and Tables

Fig. 1 The map of Olympic venues in Beijing

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Fig. 2 The 2 m temperature root mean square error (a) and bias (b) of 15 Olympic venues in Beijing provided by SVM-release, MOS-release, MM5V3 model and T213 model

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Fig. 3 The decreased root mean squre error ratio of 2 m temperature forecast from SVM-release, MOS-release to the counterpart of direct output of model

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Fig. 4 The 10 m wind u component root mean square error (a) and bias (b) of 15 Olympic venues in Beijing provided by SVM-release, MOS-release, MM5V3 model and T213 model

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Fig. 5 The 10 m wind v component root mean square error (a) and bias (b) of 15 Olympic venues in Beijing provided by SVM-release, MOS-release, MM5V3 model and T213 model

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Fig. 6 The decreased root mean square error ratio of 10 m wind u component forecast from SVM-release, MOS-release to the counterpart of direct output of model

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Fig. 7 The decreased root mean square error ratio of 10 m wind v component forecast from SVM-release, MOS-release to the counterpart of direct output of model

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Fig. 8 The 2 m specific humidity root mean square error provided by SVM-release, MOS-release (a), MM5V3 model, and the 2 m relative humidity root mean square error provided by MOS-release, T213 model (b) of 15 Olympic venues in Beijing

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Fig. 9 The decreased root mean square error ratio of 2 m specific humidity forecast from SVM-release to MM5V3 model, and the decrease root mean square error ratio of 2 m relative humidity forecast from MOS-release to T213 model

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Table 1 Selected forecast factors for SVM method

预报量	三维模式预报因子		二维模式预报因子	实况因子	因子总数
预报量	预报因子	等压面/hPa	二维模式预报因子	实况因子	因子总数
2 m温度	u和v、温度、位势高度、相对湿度、云水	975，925，850，500， 200	海平面气压、降水量	2 m温度、10 m风速、10 m 风向、过去3 h总降水量	132
10 m风u，v 分量	u和v、位势高度	975，850，500，200	海平面气压、降水量	2 m温度、本站气压、10 m 风u分量、10 m风v分量	76
10 m风u，v 分量	相对湿度	1000，975，925，850	海平面气压、降水量	2 m温度、本站气压、10 m 风u分量、10 m风v分量	76
2 m比湿	u和v、相对湿度、温度露点差	1000，975，925，850	降水量	10 m风u分量、10 m风v分量、 2 m温度露点差、2 m相对湿度	72

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