With the development and deployment of observation systems in the ocean, more precise passive and active microwave data are becoming available for the weather forecasting and the climate monitoring. Due to the complicated variability of the sea ice concentration (SIC) in the marginal ice zone and the scarcity of high-precision sea ice data, how to use less data to accurately reconstruct the sea ice field has become an urgent problem to be solved. A reconstruction method for gridding observations using the variational optimization technique, called the multi-scale high-order recursive filter (MHRF), which is a combination of Van Vliet fourth-order recursive filter and the three-dimensional variational (3D-VAR) analysis, has been designed in this study to reproduce the refined structure of sea ice field. Compared with the existing spatial multi-scale first-order recursive filter (SMRF) in which left and right filter iterative processes are executed many times, the MHRF scheme only executes the same filter process once to reduce the analysis errors caused by multiple filters and improve the filter precision. Furthermore, the series connected transfer function in the high-order recursive filter is equivalently replaced by the paralleled one, which can carry out the independent filter process in every direction in order to improve the filter efficiency. Experimental results demonstrate that this method possesses a good potential in extracting the observation information to successfully reconstruct the SIC field in computational efficiency.

The Localized Weighted Ensemble Kalman Filter (LWEnKF) is a new nonlinear/non-Gaussian data assimilation (DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height (AT-SSH), swath sea surface temperature (S-SST) and in-situ temperature and salinity (T/S) profiles are assimilated using the LWEnKF in the northern South China Sea (SCS). To adapt to the vertical S-coordinates of the Regional Ocean Modelling System (ROMS), a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF. The results show that the LWEnKF outperforms the local particle filter (LPF) due to the introduction of the Ensemble Kalman Filter (EnKF) as a proposal density; the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF, but the RMSEs of T/S profiles reduce significantly by approximately 55% for the T profile and 35% for the S profile (relative to the EnKF). As a result, the LWEnKF makes more reasonable predictions of the internal ocean temperature field. In addition, the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.

With the Regional Ocean Modeling System (ROMS), this paper investigates the sensitive areas in targeted observation for predicting the Kuroshio large meander (LM) path using the conditional nonlinear optimal perturbation approach. To identify the sensitive areas, the optimal initial errors (OIEs) featuring the largest nonlinear evolution in the LM prediction are first calculated; the resulting OIEs are localized mainly in the upper 2 500 m over the LM upstream region, and their spatial structure has certain similarities with that of the optimal triggering perturbation. Based on this spatial structure, the sensitive areas are successfully identified, located southeast of Kyushu in the region (29°–32°N, 131°–134°E). A series of sensitivity experiments indicate that both the positions and the spatial structure of initial errors have important effects on the LM prediction, verifying the validity of the sensitive areas. Then, the effect of targeted observation in the sensitive areas is evaluated through observing system simulation experiments. When targeted observation is implemented in the identified sensitive areas, the prediction errors are effectively reduced, and the prediction skill of the LM event is improved significantly. This provides scientific guidance for ocean observations related to enhancing the prediction skill of the LM event.

Empirical orthogonal function (EOF) analysis was applied to a 50-year long time series of monthly mean positions of the Kuroshio path south of Japan from a regional reanalysis. Three leading EOF modes characterize the contributions from three typical paths of the Kuroshio meander: the typical large meander path, the offshore non-large meander path, and the nearshore non-large meander path, respectively. Accordingly, the spatial variation characteristics of oceanic anomaly fields can be depicted by their regression fields upon the associated three leading principal components (PCs), which are well-matched with the results of composite analysis corresponding to each period of the three typical Kuroshio paths. A new index for the typical large meander is defined by using the second leading PC, which is highly correlated with the Kushimoto-Uragami index. Spectral analysis of this new index series shows variability of the Kuroshio path south of Japan at time scales of about 7–8 years and 20 years.

Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles. Through a dynamic scheme, a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection, and then a combined field was reconstructed by correcting the synthetic field with in situ profiles. For both schemes, a diagnostic iterative method was performed to optimize the background and observation error covariance statics. The root mean square difference (RMSD) of the in situ-only field, synthetic field and combined field were analyzed toward assimilated observations and independent observations, respectively. The rationale behind the distribution of RMSD was discussed using the following diagnostics: (1) The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters, as in the Northwest Pacific Ocean; this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity. (2) The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes; this is determined by the actual noise-to-signal ratio of ocean temperature. (3) The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics; this is determined by both the geographical feature of the synthetic field RMSD (smaller at depth in the extratropics) and that of the covariance correlation scales (smaller in the extratropics).

During the northeast monsoon season, Zhe-Min Coastal Current (ZMCC) travels along the Chinese mainland coast and carries fresh, cold, and eutrophic water. ZMCC is significantly important for the hydrodynamic processes and marine ecosystems along its path. Thus, this bottom-trapped plume deserves to be further discussed in terms of the major driving factor, for which different opinions exist. For this purpose, in this study, a high resolution Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) is established and validated. High correlation coefficients exist between along-shelf wind speeds and seasonal variations of both ZMCC volume transport and the freshwater signal. These coefficients imply that the wind is important in regulating ZMCC. However, for similar annual mean ZMCC volume transports, the extreme south boundaries of Zhe-Min Coastal Water (ZMCW) are different among different years. This difference is attracting attention and is explored in this study. According to the low wind/discharge experiment, it was found that although the volume transport of ZMCC is more sensitive to the variation of local wind speeds, the carried freshwater is limited by the Changjiang River discharge, which ultimately determines the south boundary of ZMCW. The momentum analysis at transects I and II shows that, for driving ZMCC, the along-shore wind forcing is as important as the buoyancy forcing. Note that this conclusion is supported by a zero-discharge experiment. It was also found that the buoyancy forcing varies with respect to time and space, which is due to variations of the discharge of Changjiang River. In addition, a particle tracking experiment shows that the substance carried by the Changjiang River diluted water would distribute along the Zhe-Min coastal region during the northeast monsoon season and it may escape due to the wind relaxation.

Parameter estimation is defined as the process to adjust or optimize the model parameter using observations. A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration. This assumption will cause underestimation of parameter ensemble spread, such that the parameter ensemble tends to collapse before an optimal solution is found. In this work, a two-stage inflation method is developed for parameter estimation, which can address the collapse of parameter ensemble due to the constant evolution of parameters. In the first stage, adaptive inflation is applied to the augmented states, in which the global scalar parameter is transformed to fields with spatial dependence. In the second stage, extra multiplicative inflation is used to inflate the scalar parameter ensemble to compensate for constant parameter evolution, where the inflation factor is determined according to the spread growth ratio of model states. The observation system simulation experiment with Community Earth System Model (CESM) shows that the second stage of the inflation scheme plays a crucial role in successful parameter estimation. With proper multiplicative inflation factors, the parameter estimation can effectively reduce the parameter biases, providing more accurate analyses.

This study analyzes the signal quality and the accuracy of BeiDou 3rd generation Satellite Navigation System (BDS3) Precise Point Positioning (PPP) in the Arctic Ocean. Assessment of signal quality of BDS3 includes signal to noise ratio (SNR), multipath (MP), dilution of precision (DOP), and code-minus-carrier combination (CC). The results show that, 5 to 13 satellites are visible at any time in the Arctic Ocean area as of September 2018, which are sufficient for positioning. In the mid-latitude oceanic region and in the Arctic Ocean, the SNR is 25–52 dB Hz and the MP ranges from −2 m to 2 m. As the latitude increases, the DOP values show large variation, which may be related to the distribution of BDS satellites. The CC values of signals B1I and BIC range from −5 m to 5 m in the mid-latitude sea area and the Arctic Ocean, which means the effect of pseudorange noise is small. Moreover, as to obtain the external precise reference value for GNSS positioning in the Arctic Ocean region is difficult, it is hard to evaluate the accuracy of positioning results. An improved isotropy-based protection level method based on Receiver Autonomous Integrity Monitoring is proposed in the paper, which adopts median filter to smooth the gross errors to assess the precision and reliability of PPP in the Arctic Ocean. At first, the improved algorithm is verified with the data from the International GNSS Service Station Tixi. Then the accuracy of BDS3 PPP in the Arctic Ocean is calculated based on the improved algorithm. Which shows that the kinematic accuracy of PPP can reach the decimeter level in both the horizontal and vertical directions, and it meets the precision requirements of maritime navigation.

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature (SST) forecasts have been developed in this study: a backpropagation neural network (BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function (EOF) analysis and BPNN (named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea (SCS), in which the target dataset is a six-year (2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis (CORA), and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills; however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to −3°C; now, it is minimized substantially, falling within the error range (±0.5°C) of the satellite SST data.