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    Temporal and Spatial Distribution Characteristics of Rainstorm in China Based on CMORPH Integrated Precipitation by ZHAO Zixi, CHEN Zhihe

    Published 2020-01-01
    “…Based on the data set of China automatic station and CMORPH precipitation product fusion grid,this paper analyzes the spatial-temporal distribution characteristics and change trend of rainstorm number and rainstorm amount in 2008—2016 in China by the methods of rainfall object identification and Theil-Sen estimator.The research shows that there are 24 676 rainstorms in 2008—2016,including 20 135 small rainstorms,4 230 heavy rainstorms and 311 extremely heavy rainstorms.The rainstorm events in the whole year are at most in 2012 and at least in 2011with only 42,which occur frequently from May to September,accounting for 80.8% of the whole year.In 2010,2012 and 2014,the rainstorms in the coastal areas of South China and North China accounted for a relatively high proportion,some of which reached more than 60%.In 2008,2013 and 2016,the rainstorms in the coastal areas of South China and Hubei accounted for a relatively high proportion.From May to September,the rainstorm in China is widely distributed with large amount,mainly in the Pearl River Estuary,Northeast Jiangxi,and coastal areas of Hainan,Taiwan,Zhejiang and Fujian,with an annual average monthly rainstorm of about 150 mm.The number and amount of rainstorms are decreasing from the southeast coast to the northwest inland,with high value areas mainly in South China and the middle and lower reaches of the Yangtze River.The distribution of small rainstorms is similar to that of the total rainstorms.The heavy rainstorms are mainly concentrated in the coastal areas of Guangdong and Guangxi,the eastern Hainan and the southern Taiwan.Extremely heavy rainstorms are scattered in Southeast China.In the past 9 years,the number of rainstorms has increased,especially in South China and the middle and lower reaches of the Yangtze River,with significantly increased amount.…”
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  10. 1310

    Research on Multiobjective Optimal Operation Strategy for Wind-Photovoltaic-Hydro Complementary Power System by Guo Zhao, Chenxi Wan, Wanqing Zuo, Kefei Zhang, Xinyi Shu

    Published 2022-01-01
    “…Meanwhile, this essay suggests an IMOSSA on the basis of tent chaotic sequence and random wandering strategy to settle the described triobjective optimization issue. Taking Hubei Pankou as an example for simulation analysis, after choosing the best scheme, IMOSSA compared with MOSSA, MOGWO, and NSGA-II, the volatility of sunny days is reduced by 12.39%, 19.5%, and 36.71%, respectively; the wind-photovoltaic abandonment rate is reduced by 11.17%, 22.5%, and 38.03%, respectively, while in the rainy days the volatility is reduced by 8.09%, 18.34%, and 47.03%, respectively; the wind-photovoltaic abandonment rate is reduced by 14.84%, 16.86%, and 40%, respectively. …”
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    Outbreak prediction of covid-19 in most susceptible countries by D. Yadav, H. Maheshwari, U. Chandra

    Published 2020-08-01
    “…Origin of the coronavirus was the seafood market of Wuhan city, Hubei province in China. The cases of someone suffering from COVID-19 can be traced back to the end of December 2019 in China. …”
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