17 Papers
98 Citations
Yun Liu is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Data assimilation & Sea surface temperature. The author has an hindex of 12, co-authored 15 publications. Previous affiliations of Yun Liu include University of Maryland, College Park.
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Papers
Seasonal variability of thermal fronts in the northern South China Sea from satellite data
TL;DR: The 8-year Pathfinder sea surface temperature data have been applied to produce the objectively derived seasonality of the oceanic thermal fronts in the northern South China Sea from 17 degreesN to 25 degreesN as mentioned in this paper.
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Seasonal and Long-Term Atmospheric Responses to Reemerging North Pacific Ocean Variability: A Combined Dynamical and Statistical Assessment
TL;DR: In this paper, the atmospheric response to a North Pacific subsurface oceanic temperature anomaly is studied in a coupled ocean-atmosphere general circulation model using a combined dynamical and statistical approach, with the focus on the evolution at seasonal and longer time scales.
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On the Assessment of Nonlocal Climate Feedback. Part I: The Generalized Equilibrium Feedback Assessment*
Zhengyu Liu,Na Wen,Yun Liu +2 more
TL;DR: In this article, a statistical method is developed to assess the full climate feedback of nonlocal climate feedbacks, which is a multivariate generalization of the univariate equilibrium feedback assessment (EFA) method.
Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study*
TL;DR: In this article, a new leading averaged coupled covariance (LACC) method for strongly coupled data assimilation (SCDA) is proposed, which not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled correlation between variables from different model components.
Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model
TL;DR: In this article, the impact of geographic dependence of model sensitivities on parameter optimization was explored within a twin-experiment framework using an intermediate atmosphere-ocean-land coupled model.
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