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Yongsheng Chen

Researcher at National Center for Atmospheric Research

Publications -  9
Citations -  1441

Yongsheng Chen is an academic researcher from National Center for Atmospheric Research. The author has contributed to research in topics: Weather Research and Forecasting Model & Data assimilation. The author has an hindex of 6, co-authored 9 publications receiving 1279 citations. Previous affiliations of Yongsheng Chen include York University.

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Prediction of Landfalling Hurricanes with the Advanced Hurricane WRF Model

TL;DR: This paper used the Advanced Hurricane WRF (AHW) model to forecast five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and occasionally superior to, other operational forecasts for storm position and intensity.
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The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

TL;DR: An overview of the scientific capabilities of WRFDA is provided, and together with results from sample operation implementations at the U.S. and international levels, the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date are discussed.
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Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results

TL;DR: WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation and is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments.
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Large-Eddy Simulation of an Idealized Tropical Cyclone

TL;DR: In this article, a large-eddy simulation of an idealized tropical cyclone was performed using the Advanced Research Weather Research and Forecasting (ARW) model, using six nested grids centered on the TC.
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Assimilating vortex position with an ensemble kalman filter

TL;DR: The assimilation of observations of the vortex shape and intensity, along with position, extends the technique’s effectiveness to larger displacements of the forecasted vortices and shows that the track forecast initialized with the EnKF analysis is improved.