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Pengcheng Wang, Zhongjie He, Keith R. Thompson, and Jinyu Sheng

Abstract

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.

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Zhongjie He, Yuanfu Xie, Wei Li, Dong Li, Guijun Han, Kexiu Liu, and Jirui Ma

Abstract

A recursive filter or parameterized curve fitting technique is usually used in a three-dimensional variational data assimilation (3DVAR) scheme to approximate the background error covariance, which can only represent the errors of an ocean field over a predetermined scale. Without an accurate flow-dependent error covariance that is also local and time dependent, a 3DVAR system may not provide good analyses because it is optimal only under the assumption of an accurate covariance. In this study, a sequential 3DVAR (S3DVAR) is formulated in model grid space to examine if there is useful information that can be extracted from the observation. This formulation is composed of a series of 3DVARs, each of which uses recursive filters with different length scales. It can provide an inhomogeneous and anisotropic analysis for the wavelengths that can be resolved by the observation network, just as with the conventional Barnes analysis or successive corrections. Being a variational formulation, S3DVAR can deal with data globally with an explicit specification of the observation errors; explicit physical balances or constraints; and advanced datasets, such as satellite and radar. Even though the S3DVAR analysis can be viewed as a set of isotropic functions superpositioned together, this superposition is not prespecified as in a single 3DVAR approach but is determined by the information that can be resolved by observation. The S3DVAR is adopted in a global sea surface temperature (SST) data assimilation system, into which the shipboard SSTs and the 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder daily SSTs are assimilated, respectively. The results demonstrate that the proposed S3DVAR works better in practice than a single 3DVAR.

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Wei Li, Yuanfu Xie, Zhongjie He, Guijun Han, Kexiu Liu, Jirui Ma, and Dong Li

Abstract

Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the 𝗕 matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the 𝗕 matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the 𝗕 matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate 𝗕 matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses.

A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-mean-square errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure.

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

Abstract

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.

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