Spatial and Temporal Distributions of Probability Classification of Precipitation and Temperature Anomalies over China

Yang Xiaobo; Chen Lijuan; Liu Yunyun

Vol.22, NO.5, 2011

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Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(5): 513-524

Yang Xiaobo, Chen Lijuan, Liu Yunyun. Spatial and temporal distributions of probability classification of precipitation and temperature anomalies over China. J Appl Meteor Sci, 2011, 22(5): 513-524.

Citation:

Yang Xiaobo, Chen Lijuan, Liu Yunyun. Spatial and temporal distributions of probability classification of precipitation and temperature anomalies over China. J Appl Meteor Sci, 2011, 22(5): 513-524.

Yang Xiaobo, Chen Lijuan, Liu Yunyun. Spatial and temporal distributions of probability classification of precipitation and temperature anomalies over China. J Appl Meteor Sci, 2011, 22(5): 513-524.

Citation:

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Spatial and Temporal Distributions of Probability Classification of Precipitation and Temperature Anomalies over China

Yang Xiaobo^1,2,3,
Chen Lijuan^{2
,
,},
Liu Yunyun²

1.
Institute of Plateau Meteorology, CMA, Chengdu 610072
2.
National Climate Center, Laboratory for Climate Studies of China Meteorological Administration, Beijing 100081
3.
Sichuan Provincial Climate Center, Chengdu 610072

Received Date: 2010-11-18
Rev Recd Date: 2011-06-07
Publish Date: 2011-10-31

Abstract

Abstract

Based on the standard of the probability classification definition and scoring method in short term climate prediction operation, analysis is conducted on six-level probability classification of monthly precipitation and temperature anomalies in January and July. Spatial and temporal distributions are obtained through the monthly precipitation and temperature data at 160 stations in China, which are operationally used by National Climate Center of CMA. The six levels are defined as much more than normal (L1), moderately more than normal (L2), slightly more than normal (L3), slightly less than normal (L4), moderately less than normal (L5), much less than normal (L6).The results indicate that the issued six-level probability classification is suitable for symmetrical distribution cases for positive and negative anomalies but neglecting spatial inhomogeneous distributions and inter-decadal variations of monthly temperature and precipitation. During the period of 1980—2009, the probability of L1 and L6 for precipitation in North China is high in January whereas that of L6 and L5 is elevated in South China in July. The six-level probability for precipitation in January and July is generally similar in South China. The probability of L4, L3, and L2 temperature is high whereas that of L6, L5, and L1 is low for temperature in China in both January and July. Compared to those in the period of 1951—1979, the station numbers of L1 and L2 in January and L5 for precipitation in July have significantly increased but those of L6 precipitation in January and L6 and L4 for precipitation in July have remarkably decreased in the period of 1980—2009. Meanwhile, the station numbers of L4, L5, L6 for temperature in January have substantially decreased but those of L1, L2, L3 for temperature in January increases significantly and the six-level temperature probability in July shows no variability since 1980.The above results could provide an important reference for climate forecasters to fully consider inter-decadal, inter-annual and inter-seasonal variability. The standard of the scoring method for the climate prediction focuses on the accurate rate of classification prediction, and especially emphasizes the abnormal level of precipitation and temperature. Therefore, the scoring method will help promote climate prediction services. The six-level scoring method for precipitation is more reasonable, while for temperature the method needs appropriate improvements.
- precipitation;
- temperature;
- probability classification;
- spatial and temporal distribution;
- climate prediction

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References(30)

Figures and Tables

Fig. 1 The standard deviation of precipitation percentage anomalies in China in January and July from 1980 to 2009(unit:%)

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图 2 1980—2009年1月我国降水六级概率分布 (单位：%)

(阴影区表示概率分布超过20%的区域)

Fig. 2 Distributions of the six-level probability of precipitation anomalies in January from 1980 to 2009(unit:%)

(the shaded area denotes the probability over 20%)

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Fig. 3 Distributions of the six-level probability of precipitation anomalies in July from 1980 to 2009(unit:%; others same as in Fig. 2)

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Fig. 4 The standard deviation of temperature in China in January and July from 1980 to 2009(unit:℃)

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Fig. 5 Distributions of the six-level probability of temperature anomalies in January from 1980 to 2009(unit:%; others same as in Fig. 2)

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Fig. 6 Distributions of the six-level probability of temperature anomalies in July from 1980 to 2009(unit:%; others same as in Fig. 2)

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Fig. 7 The number of stations where the six-level probability of precipitation and temperature anomalies greater than 25% in January and July

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图 8 1980—2009年与1951—1979年全国降水概率的差值分布 (单位：%)

(阴影区表示达到0.1显著性水平)

Fig. 8 Precipitation probability anomalies between the periods of 1980—2009 and 1951—1979(unit:%)

(the shaded area denotes passing the test of 0.1 level)

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图 9 1980—2009年与1951—1979年全国气温概率的差值分布 (单位：%)

(阴影区表示达到0.1显著性水平)

Fig. 9 Temperature probability differences between the periods of 1980—2009 and 1951—1979(unit:%)

(the shaded area shows passing the test of 0.1 level)

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Table 1 The terminology of six-level scoring method and the classification standards of each level for precipitation and temperature prediction

预测用语	ΔR/%	ΔT/℃
特少 (特低)	ΔR≤-50	ΔT≤-2.0
偏少 (偏低)	-50＜ΔR≤-20	-2.0＜ΔT≤-1.0
正常略少 (正常略低)	-20＜ΔR＜0	-1.0＜ΔT＜0
正常略多 (正常略高)	0≤ΔR＜20	0≤ΔT＜1.0
偏多 (偏高)	20≤ΔR＜50	1.0≤ΔT＜2.0
特多 (特高)	50≤ΔR	2.0≤ΔT
注：ΔR为降水距平百分率; ΔT为气温距平。

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Table 2 The six-level scoring method for precipitation and temperature predictions at one station

实况	预测
实况	特少 (特低)	偏少 (偏低)	正常略少 (正常略低)	正常略多 (正常略高)	偏多 (偏高)	特多 (特高)
特少 (特低)	100	80+10	60	20	0	0
偏少 (偏低)	80+10	100	80	40	20	0
正常略少 (正常略低)	60	80+10	100	60	40	20
正常略多 (正常略高)	20	40	60	100	80+10	60
偏多 (偏高)	0	20	40	80	100	80+10
特多 (特高)	0	0	20	60	80+10	100

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References

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