Xiaojing Li
Ontario Ministry of Natural Resources
24 Papers
40 Citations
Xiaojing Li is an academic researcher from Ontario Ministry of Natural Resources. The author has contributed to research in topics: Environmental science & Climatology. The author has an hindex of 5, co-authored 13 publications. Previous affiliations of Xiaojing Li include State Oceanic Administration & University of Northern British Columbia.
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Papers
A study of the effects of westerly wind bursts on ENSO based on CESM
TL;DR: In this paper, the authors introduce a westerly wind burst parameterization scheme into the global coupled Community Earth System Model (CESM) to improve the representation of wind bursts and to study the impacts of WWBs on El Nino-Southern Oscillation (ENSO) characteristics.
The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years
Ting Liu,Youmin Tang,Youmin Tang,Dejian Yang,Yanjie Cheng,Xunshu Song,Zhaolu Hou,Zheqi Shen,Yanqiu Gao,Yanling Wu,Xiaojing Li,Banglin Zhang +11 more
TL;DR: The relationships identified here exhibit considerable significant practical sense to conduct predictability researches, which provide an inexpensive and moderate approach for inquiring prediction uncertainties without the requirement of costly ensemble experiments.
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Assessment of Madden–Julian oscillation simulations with various configurations of CESM
TL;DR: In this article, the authors present an assessment of the Madden-Julian oscillation (MJO) simulated in five experiments using the Community Earth System Model under different model settings, focusing on the effects of air-sea coupling, resolution and atmospheric physics on the basic characteristics of the MJO, including intraseasonal variance, wavenumber-frequency characteristics and eastward propagation, using outgoing longwave radiation (OLR), zonal winds at 850hPa (U850) and at 200hpa (U200).
A new formulation of vector weights in localized particle filters
TL;DR: Numerical experiments show that the new localized particle filter performs better than the existing LPF algorithm, indicating the advantages and potential applications of this new algorithm of vector weights in the field of data assimilation.
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