Y
Yang Yang
Researcher at Nanjing University of Information Science and Technology
Publications - 37
Citations - 506
Yang Yang is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Eddy & Baroclinity. The author has an hindex of 9, co-authored 28 publications receiving 207 citations. Previous affiliations of Yang Yang include University of South Florida St. Petersburg.
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On the Decadal Variability of the Eddy Kinetic Energy in the Kuroshio Extension
TL;DR: In this paper, the authors investigated the physical mechanism responsible for the decadal variability of eddy kinetic energy (EKE) in the upstream Kuroshio Extension is negatively correlated with jet strength, which seems counterintuitive at first glance because linear stability analysis usually suggests that a stronger jet would favor baroclinic instability and thus lead to stronger eddy activities.
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On the Seasonal Eddy Variability in the Kuroshio Extension
Yang Yang,X. San Liang +1 more
TL;DR: Using multiscale window transform (MWT) and canonical energy transfer theory, the authors investigated the seasonal eddy variability in the Kuroshio Sea, and found that MWT-based MWT can be used to transfer energy from one season to another.
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The Instabilities and Multiscale Energetics Underlying the Mean–Interannual–Eddy Interactions in the Kuroshio Extension Region
Yang Yang,X. San Liang +1 more
TL;DR: In this paper, the authors investigated the intricate nonlinear mutual interactions among the decadally modulating mean flow, the interannual fluctuations, and the transient eddies in the Kuroshio Extension region.
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Co-occurrence of ozone and PM2.5 pollution in the Yangtze River Delta over 2013–2019: Spatiotemporal distribution and meteorological conditions
TL;DR: In this paper, the spatial-temporal variations of surface layer ozone (O3) and PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5μm or less) observed from April 2013 to December 2019 in the Yangtze River Delta (YRD) region to identify the O3-PM2.
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Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach.
TL;DR: A gridded near-surface PM2.5 concentration dataset across China covering 1980-2019 is constructed using the space-time random forest model with atmospheric visibility observations and other auxiliary data to investigate interannual and decadal environmental and climate impacts related to aerosols.