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Qingyun Duan
Researcher at Hohai University
Publications - 213
Citations - 26241
Qingyun Duan is an academic researcher from Hohai University. The author has contributed to research in topics: Climate change & Precipitation. The author has an hindex of 62, co-authored 196 publications receiving 21640 citations. Previous affiliations of Qingyun Duan include University of Arizona & Beijing Normal University.
Papers
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Journal ArticleDOI
Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area
Zhenhua Di,Zhenhua Di,Qingyun Duan,Wei Gong,Chen Wang,Chen Wang,Yanjun Gan,Jiping Quan,Jianduo Li,Chiyuan Miao,Aizhong Ye,Charles Tong +11 more
TL;DR: In this paper, a global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting.
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Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China
Feng Ma,Aizhong Ye,Xiaoxue Deng,Zheng Zhou,Xiaojie Liu,Qingyun Duan,Jing Xu,Chiyuan Miao,Zhenhua Di,Wei Gong +9 more
TL;DR: In this paper, the authors evaluated the performance of the 11 models from the North American Multi-Model Ensemble (NMME) to forecast seasonal precipitation over 17 hydroclimatic regions in continental China.
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Long-term trends in global river flow and the causal relationships between river flow and ocean signals
TL;DR: In this paper, the Mann-Kendall (MK) test was used to detect the long-term flow trends in 916 of the world's largest ocean-reaching rivers over the period 1948-2004.
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Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans
TL;DR: In this paper, the authors used the Granger causality test to examine the effects of the El Nino-Southern Oscillation (ENSO), the Pacific Decadal Ontology (PDO), and the North Atlantic Oscillations (NAO) on global dry/wet conditions.
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A GUI platform for uncertainty quantification of complex dynamical models
TL;DR: A flexible software platform designed to quantify uncertainty of complex dynamical models, UQ-PyL (Uncertainty Quantification Python Laboratory), which is written in Python language and runs on all common operating systems is described.