Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Zhongjie He x
  • Refine by Access: All Content x
Clear All Modify Search
Pengcheng Wang, Zhongjie He, Keith R. Thompson, and Jinyu Sheng


Near-inertial oscillations (NIOs) on the inner Scotian shelf are studied using observations, a simple slab model, and two operational shelf circulation models. High-frequency radar and ADCP observations from December 2015 to February 2016 show that individual NIO events forced by time-varying wind stress typically lasted for three to four inertial periods. NIOs with speeds exceeding 0.25 m s−1 were observed in the offshore part of the study region, but their amplitudes decreased shoreward within ~40 km of the coast. The NIOs had spatial scales of ~80 and ~40 km in the alongshore and cross-shore directions, respectively. The NIO phases varied moving from west to east, consistent with the typical movement of winter storms across the study region. Evolving rotary spectral analysis reveals that the peak frequency f p of the NIOs varied with time by ~7% of the local inertial frequency. The variation in f p can be explained in part by local wind forcing as demonstrated by the slab model. The remaining variation in f p can be explained in part by variations in the background vorticity associated with changes in the strength and position of the Nova Scotia Current, an unstable baroclinic boundary current that runs along the coast to the southwest. Two operational shelf circulation models are used to examine the abovementioned features in the high-frequency-radar and ADCP observations. The models reproduce the spatial structure of the NIOs and, in a qualitative sense, the temporal variations of f p.

Full access
Yuxin Zhao, Dequan Yang, Wei Li, Chang Liu, Xiong Deng, Rixu Hao, and Zhongjie He


A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.

Restricted access