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Jidong Gao

Researcher at National Oceanic and Atmospheric Administration

Publications -  86
Citations -  3535

Jidong Gao is an academic researcher from National Oceanic and Atmospheric Administration. The author has contributed to research in topics: Data assimilation & Radar. The author has an hindex of 29, co-authored 76 publications receiving 3103 citations. Previous affiliations of Jidong Gao include University of Oklahoma.

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The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation

TL;DR: The Advanced Regional Prediction System of the Center for Analysis and Prediction of Storms at the University of Oklahoma as discussed by the authors was used to predict a series of supercell storms that produced a historical number of tornadoes more than 8 hours in advance to within tens of kilometers in space.
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A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars

TL;DR: In this paper, a new method of dual-Doppler radar wind analysis based on a three-dimensional variational data assimilation (3DVAR) approach is proposed, where a cost function, including background term and radial observation term, is minimized through a limited memory, quasi-Newton conjugate-gradient algorithm with the mass continuity equation imposed as a weak constraint.
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A Variational Method for the Analysis of Three-Dimensional Wind Fields from Two Doppler Radars

TL;DR: This paper proposes a new method of dual-Doppler radar analysis based on a variational approach, in which a cost function is minimized through a limited memory, quasi-Newton conjugate gradient algorithm with the mass continuity equation imposed as a weak constraint.
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3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR

TL;DR: In this paper, the impact of radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied.