Sound speed profiles in high spatiotemporal resolution using multigrid three-dimensional variational method:a coastal experiment off northern Shandong Peninsula

It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic activities.However,conventional observation methods cannot obtain high-resolution SSPs.Besides,S SPs are complex and changeable in time and space,especially in coastal areas.We proposed a new space-time multigrid three-dimensional variational method with weak constraint term(referred to as STC-MG3DVar)to construct high-precision spatiotemporal resolution SSPs in coastal areas,in which sound velocity is defined as the analytical variable,and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the STC-MG3DVar.The spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method,and the multi-scale information of sound velocity observations from long waves to short waves can be successively extracted.The weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate SSPs.To verify the accuracy of the STC-MG3DVar,SSPs observations and CTD observations(temperature observations,salinity observations)are obtained from field experiments in the northern coastal area of the Shandong Peninsula.The average root mean square error(RMSE)of the STC-MG3DVar-constructed SSPs is 0.132 m/s,and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term(ST-MG3DVar)by 10.14%and over the spatial multigrid 3DVar with weak constraint term(SC-MG3DVar)by 44.19%.With the advantage of the constraint term and the spatiotemporal correlation information,the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SCMG3DVar in constructing high-precision spatiotemporal re solution SSPs.

A Regional Ocean Reanalysis System for Coastal Waters of China and Adjacent Seas

A regional ocean reanalysis system for the coastal waters of China and adjacent seas has been developed by the National Marine Data and Information Service（NMDIS）.It produces a dataset package called CORA （China ocean reanalysis）.The regional ocean model used is based on the Princeton Ocean Model with a generalized coordinate system（POMgcs）.The model is parallelized by NMDIS with the addition of the wave breaking and tidal mixing processes into model parameterizations.Data assimilation is a sequential three-dimensional variational（3D-Var） scheme implemented within a multigrid framework.Observations include satellite remote sensing sea surface temperature（SST）,altimetry sea level anomaly（SLA）,and temperature/salinity profiles.The reanalysis fields of sea surface height,temperature,salinity,and currents begin with January 1986 and are currently updated every year. Error statistics and error distributions of temperature,salinity and currents are presented as a primary evaluation of the reanalysis fields using sea level data from tidal gauges,temperature profiles,as well as the trajectories of Argo floats.Some case studies offer the opportunity to verify the evolution of certain local circulations.These evaluations show that the reanalysis data produced provide a good representation of the ocean processes and phenomena in the coastal waters of China and adjacent seas.

Bias correction of sea surface temperature retrospective forecasts in the South China Sea

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.

Temporal-spatial oceanic variation in relation with the three typical Kuroshio paths south of Japan

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 nonlarge 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.

China Ocean ReAnalysis(CORA)version 1.0 products and validation for 2009-18

China Ocean ReAnalysis(CORA) version 1.0 products for the period 2009-18 have been developed and validated.The model configuration and assimilation algorithm have both been updated compared to those of the 51-year(1958-2008) products.The assimilated observations include temperature and salinity field data,satellite remote sensing sea surface temperature,and merged sea surface height(SSH) anomaly data.The validation includes the following three aspects:(1) Temperature,salinity,and SSH anomaly root-mean-square errors(RMSEs) are computed as a primary evaluation of the reanalysis quality.The 0-2000 m domain-averaged RMSEs of temperature and salinity are 0.61℃ and 0.08 psu,respectively.The SSH anomaly RMSE is less than 0.2 m in most regions.(2) The 35°N temperature section is used to evaluate the ability to reproduce the thermocline,mixing layer,and Yellow Sea cold water mass.In summer,the thermocline is reinforced,with the gradient changing from 3℃ in May to 10℃ in August.The mixing-layer depth reproduced by CORA is consistent with that computed from the observed climatology.The Yellow Sea cold water mass forms at a depth of 50 m.(3) The reanalysis current is examined against the tracks of some drifting buoys.The results show that the reanalysis current can capture the mesoscale eddies near the Kuroshio,which are similar to those described by the drifting buoys.Overall,the 2009-18 CORA reanalysis products are capable of reproducing major oceanic phenomena and processes in the coastal waters of China and adjacent seas.

An application of the A-4DEnVar to coupled parameter optimization

In variational methods,coupled parameter optimization(CPO) often needs a long minimization time window(MTW) to fully incorporate observational information,but the optimal MTW somehow depends on the model nonlinearity.The analytical four-dimensional ensemble-variational(A-4DEnVar) considers model nonlinearity well and avoids adjoint model.It can theoretically be applied to CPO.To verify the feasibility and the ability of the A-4DEnVar in CPO,“twin” experiments based on A-4DEnVar CPO are conducted for the first time with the comparison of four-dimensional variational(4D-Var).Two algorithms use the same background error covariance matrix and optimization algorithm to control variates.The experiments are based on a simple coupled oceanatmosphere model,in which the atmospheric part is the highly nonlinear Lorenz-63 model,and the oceanic part is a slab ocean model.The results show that both A-4DEnVar and 4D-Var can effectively reduce the error of state variables through CPO.Besides,two methods produce almost the same results in most cases when the MTW is less than 560 time steps.The results are similar when the MTW is larger than 560 time steps and less than 880 time steps.The largest MTW of 4 D-Var and A-4DEnVar are 1 200 time steps.Moreover,A-4DEnVar is not sensitive to ensemble size when the MTW is less than 720 time steps.A-4DEnVar obtains satisfactory results in the case of highly nonlinear model and long MTW,suggesting that it has the potential to be widely applied to realistic CPO.