Application of MOS Method on Pentad Mean Temperature Prediction in Dynamical Extended Range

Chen Yuying; Chen Nan; Wang Suyan; Shao Jian; Mu Jianhua; Na Li

Vol.22, NO.1, 2011

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Chen Yuying, Chen Nan, Wang Suyan, et al. Application of MOS method on pentad mean temperature prediction in dynamical extended range. J Appl Meteor Sci, 2011, 22(1): 86-95.

Citation:

Chen Yuying, Chen Nan, Wang Suyan, et al. Application of MOS method on pentad mean temperature prediction in dynamical extended range. J Appl Meteor Sci, 2011, 22(1): 86-95.

Chen Yuying, Chen Nan, Wang Suyan, et al. Application of MOS method on pentad mean temperature prediction in dynamical extended range. J Appl Meteor Sci, 2011, 22(1): 86-95.

Citation:

Chen Yuying, Chen Nan, Wang Suyan, et al. Application of MOS method on pentad mean temperature prediction in dynamical extended range. J Appl Meteor Sci, 2011, 22(1): 86-95.

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Application of MOS Method on Pentad Mean Temperature Prediction in Dynamical Extended Range

Chen Yuying^{1,2
,
,},
Chen Nan^1,2,
Wang Suyan¹,
Shao Jian¹,
Mu Jianhua¹,
Na Li¹

1.
Key Laboratory for Meteorological Disaster Prevention and Reduction of Ningxia, Yinchuan 750002
2.
Ningxia Meteorological Observatory, Yinchuan 750002

Received Date: 2010-05-12
Rev Recd Date: 2010-12-06
Publish Date: 2011-02-28

Abstract

Abstract

Stepwise regression MOS statistical method is applied to predict the pentad mean temperature of future 40 days at 24 weather stations in Ningxia, using the pentad mean temperature and dynamical extended range forecasting products from January 1982 to March 2010. In order to evaluate the predicting capability of the direct numerical model output products (DMO) and MOS which use the seasonal and monthly data, the prediction results of DMO and MOS from January to March in 2009 and 2010 are compared.The prediction accuracy and stability of MOS improved remarkably comparing with DMO, MOS method can predict the trend and extent of violent weather changes in temperature. With the drawing near of predicting time and successive correction, its prediction error values decreases gradually, and predicting results can be for the reference of medium term prediction operation.MOS prediction capability is different when using data of different lengths. The prediction capability of MOS using monthly data is better, because the chosen prediction factors using monthly data can better indicate the correlation of prediction objects in this period, and its physical meaning is much distinct. So more monthly data samples lead to better temperature prediction result and stability.MOS method merely applies the output products of dynamical extended range prediction model, so its prediction results thoroughly rely on the accuracy and stability of numerical prediction model. The prediction results may be more accurate if some observation factor, local experimental factor and climatic factor are added. There are 4 aspects which need attentions when establishing MOS equations: The equations should be established based on experimental calculating error values; the establishment of F-test value at each weather station should also consider experimental calculating error values; using data samples of longer temporal scales to verify if MOS prediction results will be better using more monthly data samples; finally, more new data are recommended to improve MOS prediction capability in the future.
- MOS method;
- dynamical extended range prediction model;
- pentad mean temperature;
- evaluation of prediction capability

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References(33)

Figures and Tables

Fig. 1 Distribution of mean absolute error and RMS error of pentad mean temperature, TS in Ningxia of 2009

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Fig. 2 Distribution of pentad mean temperature anomoly correlation coefficient and anomoly sign consistent rate in Ningxia of 2009

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Fig. 3 Monthly distribution of evaluation results of pentad mean temperature prediction in Ningxia of 2009

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Fig. 4 Distribution of mean absolute error and RMS error of pentad mean temperature, TS in Ningxia from Jan to Mar in 2010

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Fig. 5 Distribution of pentad mean temperature anomoly correlation coefficient and anomoly sign consistent rate from Jan to Mar in Ningxia of 2010

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Fig. 6 Distribution of evaluation results of pentad mean temperature prediction in Ningxia from Jan to Mar in 2010

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Fig. 7 Evaluation of DMO and MOS3 on the prediciton of extrem pentad mean temperature in Ningxia

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Table 1 The evaluation of 3 prediction results on pentad mean temperature prediciton in Ningxia of 2009

统计量	预报	第1候	第2候	第3候	第4候	第5候	第6候	第7候	第8候	平均
\|x\|/℃	DMO	5.09	5.30	5.60	5.83	6.12	6.17	6.32	4.37	5.60
	MOS1	1.73	1.66	1.92	1.88	1.59	1.73	1.64	1.61	1.72
	MOS2	1.21	1.48	1.46	1.66	1.44	1.52	1.32	1.34	1.43
z/℃	DMO	7.96	8.17	8.48	8.71	9.04	8.94	9.09	6.84	8.40
	MOS1	2.99	2.89	3.33	3.39	3.09	3.16	2.96	3.22	3.13
	MOS2	2.25	2.59	2.62	3.05	2.92	2.88	2.48	2.53	2.67
r	DMO	-0.74	-0.63	-0.66	-0.45	-0.55	-0.57	-0.69	-0.67	-0.62
	MOS1	-0.55	-0.53	-0.39	-0.36	-0.49	-0.49	-0.53	-0.42	-0.47
	MOS2	-0.17	-0.21	-0.16	-0.14	-0.13	-0.13	-0.11	0.06	-0.12
p/%	DMO	28.52	30.18	28.26	30.43	29.41	33.82	29.73	28.96	29.91
	MOS1	30.75	31.52	38.55	36.57	30.37	37.85	36.89	36.38	34.86
	MOS2	54.65	51.75	51.51	51.81	53.44	53.44	53.93	50.54	52.63
TS评分	DMO	0.20	0.20	0.18	0.17	0.15	0.16	0.14	0.26	0.18
	MOS1	0.66	0.68	0.62	0.63	0.72	0.66	0.70	0.70	0.67
	MOS2	0.81	0.71	0.71	0.69	0.76	0.71	0.76	0.75	0.74

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Table 2 The evaluation of two schemes of MOS method on pentad mean temperature prediciton in Ningxia from Jan to Mar in 2010

统计量	预报	第1候	第2候	第3候	第4候	第5候	第6候	第7候	第8候	平均
\|x\|/℃	MOS2	1.72	1.80	1.72	2.37	2.60	2.57	2.98	2.38	2.27
\|x\|/℃	MOS3	1.43	1.23	1.49	2.21	1.45	1.64	2.26	2.52	1.78
z/℃	MOS2	2.85	3.27	3.02	3.66	4.01	4.08	4.38	3.73	3.63
z/℃	MOS3	2.63	2.20	2.42	3.50	2.45	2.84	3.90	4.41	3.04
r	MOS2	-0.71	-0.82	-0.66	-0.54	-0.60	-0.49	-0.46	-0.53	-0.60
r	MOS3	-0.04	-0.47	-0.15	-0.10	-0.14	-0.23	-0.33	-0.33	-0.22
p/%	MOS2	30.69	37.08	41.18	38.87	44.50	44.50	50.90	26.60	39.29
p/%	MOS3	46.55	44.50	58.06	57.80	57.80	56.78	65.73	44.50	53.97
TS评分	MOS2	0.65	0.60	0.69	0.55	0.55	0.53	0.45	0.56	0.57
TS评分	MOS3	0.71	0.75	0.68	0.55	0.75	0.71	0.61	0.54	0.66

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