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New Predictors and a Statistical Forecast Model for Mei-Yu Onset Date in the Middle and Lower Reaches of the Yangtze River Valley

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  • 1 College of Meteorology and Oceanography, People's Liberation Army University of Science and Technology, Nanjing, China
  • | 2 College of Meteorology and Oceanography, People's Liberation Army University of Science and Technology, and School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • | 3 College of Meteorology and Oceanography, People's Liberation Army University of Science and Technology, Nanjing, China
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Abstract

The prediction of mei-yu onset date (MOD) in the middle and lower reaches of the Yangtze River valley (MLYRV) is an important and challenging task for those making seasonal climate predictions in China. In this paper, the atmospheric and oceanic conditions in the preceding winter and spring related to MOD are analyzed. It is found that the MOD is associated with the intensity of the Ural high and the East Asian trough in high latitudes, with the intensity of the upper-level westerly jet in middle latitudes, and with the contrast of land–sea temperature and pressure in the preceding winter and spring, which are proxies for the intensity of the East Asian winter monsoon (EAWM). It is suggested that the intensity of the EAWM is the most crucial factor affecting the MOD. Years with an early MOD usually correspond to strong EAWMs in the preceding winter, and vice versa. The EAWM can affect the MOD by influencing the East Asian summer monsoon (EASM) through tropical ocean–atmosphere and tropical–extratropical interactions. Based on the above analysis, a physics-based statistical forecast model is established using multivariable linear regression techniques. The hindcast of MOD during the 13 yr from 1998 to 2010 is carried out to evaluate the performance of this forecast model. The MOD can be predicted successfully in 8 out of the 13 yr. The forecast model predicts the MOD in the years with strong mei-yu intensity more accurately than in those with weak mei-yu intensity, especially for cases of extreme flooding. This is useful in the prevention of flooding disasters.

Corresponding author address: Yimin Zhu, Dept. of Meteorology, College of Meteorology and Oceanography, PLA University of Science and Technology, Zhong Hua Men Wai, 60 Shuanglong Street, Nanjing 211101, China. E-mail: zhuym@21cn.com

Abstract

The prediction of mei-yu onset date (MOD) in the middle and lower reaches of the Yangtze River valley (MLYRV) is an important and challenging task for those making seasonal climate predictions in China. In this paper, the atmospheric and oceanic conditions in the preceding winter and spring related to MOD are analyzed. It is found that the MOD is associated with the intensity of the Ural high and the East Asian trough in high latitudes, with the intensity of the upper-level westerly jet in middle latitudes, and with the contrast of land–sea temperature and pressure in the preceding winter and spring, which are proxies for the intensity of the East Asian winter monsoon (EAWM). It is suggested that the intensity of the EAWM is the most crucial factor affecting the MOD. Years with an early MOD usually correspond to strong EAWMs in the preceding winter, and vice versa. The EAWM can affect the MOD by influencing the East Asian summer monsoon (EASM) through tropical ocean–atmosphere and tropical–extratropical interactions. Based on the above analysis, a physics-based statistical forecast model is established using multivariable linear regression techniques. The hindcast of MOD during the 13 yr from 1998 to 2010 is carried out to evaluate the performance of this forecast model. The MOD can be predicted successfully in 8 out of the 13 yr. The forecast model predicts the MOD in the years with strong mei-yu intensity more accurately than in those with weak mei-yu intensity, especially for cases of extreme flooding. This is useful in the prevention of flooding disasters.

Corresponding author address: Yimin Zhu, Dept. of Meteorology, College of Meteorology and Oceanography, PLA University of Science and Technology, Zhong Hua Men Wai, 60 Shuanglong Street, Nanjing 211101, China. E-mail: zhuym@21cn.com
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