Baiping Wang

Dr. Baiping Wang


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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BWBaiping Wang
Baiping Wang
BJBo JIANG
Bo JIANG
JDJie Ding
Jie Ding
GZGuanming Zeng
Guanming Zeng
YTYujia Tang
Yujia Tang
Reliable characterization of dependent wind–wave extremes is essential for offshore wind engineering in monsoon- and tropical-cyclone-affected seas. Using a 2002–2020 COAWST hindcast for an offshore site in the northern South China Sea, we analyze 161,400 common-valid hourly samples of 10-m wind speed (U10), significant wave height (Hs), and peak wave period (Tp) and construct 221 temporally consistent monthly extreme pairs. Validation shows that the hindcast captures temporal co-variability but systematically underestimates wind and wave magnitudes, especially severe tropical-cyclone winds and high sea states; long-return-period estimates are therefore interpreted as methodological sensitivity measures rather than design loads. SJ-DPI and upper-tail sensitivity models based on Kernel–Pareto and GEV marginals are compared to quantify extrapolation uncertainty, while the primary uncertainty interpretation avoids relying on potentially unstable far-tail fits from limited monthly extremes. Under a common maximum pseudo-likelihood framework, no copula family is uniformly optimal, and AND-type exceedance is more sensitive to dependence-family choice than OR-type exceedance. Cool- and warm-season distributions differ significantly, but a 1000-split placebo test shows that part of the apparent long-return-period shift is attributable to the smaller-sample SJ-DPI bandwidths rather than seasonality alone. These results highlight the need to jointly consider concurrent sampling, marginal-tail specification, dependence uncertainty, and seasonal heterogeneity.