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Your search keyword '"Shi, Chunxiang"' showing total 418 results
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418 results on '"Shi, Chunxiang"'

1. Remote effects of Tibetan Plateau spring land temperature on global subseasonal to seasonal precipitation prediction and comparison with effects of sea surface temperature: the GEWEX/LS4P Phase I experiment

Author

Xue, Yongkang, Diallo, Ismaila, Boone, Aaron A., Zhang, Yang, Zeng, Xubin, Lau, William K. M., Neelin, J. David, Yao, Tandong, Tang, Qi, Sato, Tomonori, Koo, Myung-Seo, Vitart, Frederic, Ardilouze, Constantin, Saha, Subodh K., Materia, Stefano, Lin, Zhaohui, Takaya, Yuhei, Yang, Jing, Nakamura, Tetsu, Qi, Xin, Qin, Yi, Nobre, Paulo, Senan, Retish, Wang, Hailan, Zhang, Hongliang, Zhao, Mei, Nayak, Hara Prasad, Pan, Yan, Pan, Xiaoduo, Feng, Jinming, Shi, Chunxiang, Xie, Shaocheng, Brunke, Michael A., Bao, Qing, Bottino, Marcus Jorge, Fan, Tianyi, Hong, Songyou, Lin, Yanluan, Peano, Daniele, Zhan, Yanling, Mechoso, Carlos R., Ren, Xuejuan, Balsamo, Gianpaolo, Chou, Sin Chan, de Rosnay, Patricia, van Oevelen, Peter J., Klocke, Daniel, Ek, Michael, Li, Xin, Guo, Weidong, Zhu, Yuejian, Tang, Jianping, Liang, Xin-Zhong, Qian, Yun, and Zhao, Ping

Published
2024
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2. Remote effects of Tibetan Plateau spring land temperature on global subseasonal to seasonal precipitation prediction and comparison with effects of sea surface temperature: the GEWEX/LS4P Phase I experiment

Author

Xue, Yongkang, Diallo, Ismaila, Boone, Aaron A, Zhang, Yang, Zeng, Xubin, Lau, William KM, Neelin, J David, Yao, Tandong, Tang, Qi, Sato, Tomonori, Koo, Myung-Seo, Vitart, Frederic, Ardilouze, Constantin, Saha, Subodh K, Materia, Stefano, Lin, Zhaohui, Takaya, Yuhei, Yang, Jing, Nakamura, Tetsu, Qi, Xin, Qin, Yi, Nobre, Paulo, Senan, Retish, Wang, Hailan, Zhang, Hongliang, Zhao, Mei, Nayak, Hara Prasad, Pan, Yan, Pan, Xiaoduo, Feng, Jinming, Shi, Chunxiang, Xie, Shaocheng, Brunke, Michael A, Bao, Qing, Bottino, Marcus Jorge, Fan, Tianyi, Hong, Songyou, Lin, Yanluan, Peano, Daniele, Zhan, Yanling, Mechoso, Carlos R, Ren, Xuejuan, Balsamo, Gianpaolo, Chou, Sin Chan, de Rosnay, Patricia, van Oevelen, Peter J, Klocke, Daniel, Ek, Michael, Li, Xin, Guo, Weidong, Zhu, Yuejian, Tang, Jianping, Liang, Xin-Zhong, Qian, Yun, and Zhao, Ping

Subjects
Atmospheric Sciences, Oceanography, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences
Abstract

Abstract: The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

Published
2023

5. Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation: Initialization and Improved Prediction

Author

Xue, Yongkang, Diallo, Ismaila, Boone, Aaron A, Yao, Tandong, Zhang, Yang, Zeng, Xubin, Neelin, J David, Lau, William KM, Pan, Yan, Liu, Ye, Pan, Xiaoduo, Tang, Qi, Oevelen, Peter J van, Sato, Tomonori, Koo, Myung-Seo, Materia, Stefano, Shi, Chunxiang, Yang, Jing, Ardilouze, Constantin, Lin, Zhaohui, Qi, Xin, Nakamura, Tetsu, Saha, Subodh K, Senan, Retish, Takaya, Yuhei, Wang, Hailan, Zhang, Hongliang, Zhao, Mei, Nayak, Hara Prasad, Chen, Qiuyu, Feng, Jinming, Brunke, Michael A, Fan, Tianyi, Hong, Songyou, Nobre, Paulo, Peano, Daniele, Qin, Yi, Vitart, Frederic, Xie, Shaocheng, Zhan, Yanling, Klocke, Daniel, Leung, Ruby, Li, Xin, Ek, Michael, Guo, Weidong, Balsamo, Gianpaolo, Bao, Qing, Chou, Sin Chan, Rosnay, Patricia de, Lin, Yanluan, Zhu, Yuejian, Qian, Yun, Zhao, Ping, Tang, Jianping, Liang, Xin-Zhong, Hong, Jinkyu, Ji, Duoying, Ji, Zhenming, Qiu, Yuan, Sugimoto, Shiori, Wang, Weicai, Yang, Kun, and Yu, Miao

Subjects
Atmosphere, Atmosphere-land interaction, Ensembles, Numerical weather prediction, forecasting, General circulation models, Model initialization, Astronomical and Space Sciences, Atmospheric Sciences, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences
Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the "Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction"(LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global "hotspot"regions identified here; the ensemble means in some "hotspots"produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Published
2022

10. Supplementary material to "Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design"

Author

Xue, Yongkang, Yao, Tandong, Boone, Aaron A, Diallo, Ismaila, Liu, Ye, Zeng, Xubin, Lau, William K-M, Sugimoto, Shiori, Tang, Qi, Pan, Xiaoduo, van Oevelen, Peter J, Klocke, Daniel, Koo, Myung-Seo, Lin, Zhaohui, Takaya, Yuhei, Sato, Tomonori, Ardilouze, Constantin, Saha, Subodh K, Zhao, Mei, Liang, Xin-Zhong, Vitart, Frederic, Li, Xin, Zhao, Ping, Neelin, David, Guo, Weidong, Yu, Miao, Qian, Yun, Shen, Samuel SP, Zhang, Yang, Yang, Kun, Leung, Ruby, Yang, Jing, Qiu, Yuan, Brunke, Michael A, Chou, Sin Chan, Ek, Michael, Fan, Tianyi, Guan, Hong, Lin, Hai, Liang, Shunlin, Materia, Stefano, Nakamura, Tetsu, Qi, Xin, Senan, Retish, Shi, Chunxiang, Wang, Hailan, Wei, Helin, Xie, Shaocheng, Xu, Haoran, Zhang, Hongliang, Zhan, Yanling, Li, Weiping, Shi, Xueli, Nobre, Paulo, Qin, Yi, Dozier, Jeff, Ferguson, Craig R, Balsamo, Gianpaolo, Bao, Qing, Feng, Jinming, Hong, Jinkyu, Hong, Songyou, Huang, Huilin, Ji, Duoying, Ji, Zhenming, Kang, Shichang, Lin, Yanluan, Liu, Weiguang, Muncaster, Ryan, Pan, Yan, Peano, Daniele, de Rosnay, Patricia, Takahashi, Hiroshi G, Tang, Jianping, Wang, Guiling, Wang, Shuyu, Wang, Weicai, Zhou, Xu, and Zhu, Yuejian

Published
2021

11. Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design

Author

Xue, Yongkang, Yao, Tandong, Boone, Aaron A, Diallo, Ismaila, Liu, Ye, Zeng, Xubin, Lau, William K-M, Sugimoto, Shiori, Tang, Qi, Pan, Xiaoduo, van Oevelen, Peter J, Klocke, Daniel, Koo, Myung-Seo, Lin, Zhaohui, Takaya, Yuhei, Sato, Tomonori, Ardilouze, Constantin, Saha, Subodh K, Zhao, Mei, Liang, Xin-Zhong, Vitart, Frederic, Li, Xin, Zhao, Ping, Neelin, David, Guo, Weidong, Yu, Miao, Qian, Yun, Shen, Samuel SP, Zhang, Yang, Yang, Kun, Leung, Ruby, Yang, Jing, Qiu, Yuan, Brunke, Michael A, Chou, Sin Chan, Ek, Michael, Fan, Tianyi, Guan, Hong, Lin, Hai, Liang, Shunlin, Materia, Stefano, Nakamura, Tetsu, Qi, Xin, Senan, Retish, Shi, Chunxiang, Wang, Hailan, Wei, Helin, Xie, Shaocheng, Xu, Haoran, Zhang, Hongliang, Zhan, Yanling, Li, Weiping, Shi, Xueli, Nobre, Paulo, Qin, Yi, Dozier, Jeff, Ferguson, Craig R, Balsamo, Gianpaolo, Bao, Qing, Feng, Jinming, Hong, Jinkyu, Hong, Songyou, Huang, Huilin, Ji, Duoying, Ji, Zhenming, Kang, Shichang, Lin, Yanluan, Liu, Weiguang, Muncaster, Ryan, Pan, Yan, Peano, Daniele, de Rosnay, Patricia, Takahashi, Hiroshi G, Tang, Jianping, Wang, Guiling, Wang, Shuyu, Wang, Weicai, Zhou, Xu, and Zhu, Yuejian

Abstract

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called Impact of initialized Land Surface temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land/atmosphere interactions. LS4P focuses on process understanding and predictability, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews the history and objectives of LS4P, provides the first phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations which both tend to limit the soil memory; and ii) reanalysis data, that are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Published
2021

16. Research on coal and rock recognition model based on improved 1DCNN

Author

YIN Yuxi, ZHOU Changfei, XU Zhipeng, SHI Chunxiang, and HU Wenyuan

Subjects
coal and rock recognition, convolutional neural network, 1dcnn, vibration signal, cosine annealing, t-distributed stochastic neighbor embedding, Mining engineering. Metallurgy, TN1-997
Abstract

With the acceleration of intelligent construction of coal mines, efficient recognition of coal and rock has become a technical problem to be solved urgently in intelligent coal mining. The existing coal and rock recognition methods under complex coal mine geological conditions have problems of low precision, poor universality and are difficult to apply in engineering. In order to solve the above problems, a coal and rock recognition model based on improved 1-dimensional convolutional neural network (1DCNN) is proposed. Based on the 1DCNN, a plurality of continuous convolution layers are used for extracting one-dimensional vibration signal features. The global average pool (GAP) layer is used for replacing the full connection layer. The model training parameters are reduced, and computing resources are saved. At the same time, a cosine annealing attenuation method with a linear hot start is adopted for optimizing the learning rate. Therefore, the model training is prevented from falling into a local minimum region, and the training quality is improved. In order to intuitively describe the feature extraction process and classification capability of the improved 1DCNN model for coal and rock cutting vibration data, the t-distributed stochastic neighbor embedding (t-SNE) manifold learning algorithm is used to visually analyze the feature learning process of the model. The results show that the improved 1DCNN model can realize the recognition of coal and rock cutting states well through feature learning layer by layer. Based on the measured vibration data obtained in the process of coal and rock cutting of the MG 650/1590-WD shearer in a mine in Shaanxi province, the model is trained and the result shows that the accuracy of the improved 1DCNN model is 99.91% on the training set and 99.32% on the test set. The model can be directly used to classify the original vibration signals of the shearer in coal and rock cutting, and can effectively identify the cutting state of coal and rock. Compared with traditional machine learning, ensemble learning and the unmodified 1DCNN model, the improved 1DCNN model has obvious advantages. The average recognition accuracy rate reaches 99.56%. The calculation cost is greatly saved, and the model recognition speed is improved.

Published
2023
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48. chapter 16: ANTHROPOGENIC CONTRIBUTIONS TO THE 2018 EXTREME FLOODING OVER THE UPPER YELLOW RIVER BASIN IN CHINA: Anthropogenic climate change, reservoir operation, and land cover change have decreased the risk of 2018 extreme summer flooding by 34%, 45%, and 11% respectively

Author

Ji, Peng, Yuan, Xing, Jiao, Yang, Wang, Chunqing, Han, Shuai, and Shi, Chunxiang

Subjects
Yellow River (China) -- Environmental aspects, Floods -- Environmental aspects -- China, Climate change -- Environmental aspects, Business, Earth sciences
Abstract

The Upper Yellow River basin (UYRB), located over northwestern China and featuring a semiarid climate, experienced extreme flooding during the summer and autumn of 2018, with June-September rainfall ranked the [...]

Published
2020
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