Cycling the representer algorithm for variational data assimilation with a nonlinear reduced gravity ocean model

The representer method was used by [Ngodock, H.E., Jacobs, G.A., Chen, M., 2006. The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: a comparison study using a nonlinear reduced gravity ocean model. Ocean Modelling 12, 378–400] in a comparison study with the ensemble Kalman filter and smoother involving a 1.5 nonlinear reduced gravity idealized ocean model simulating the Loop Current (LC) and the Loop Current eddies (LCE) in the Gulf of Mexico. It was reported that the representer method was more accurate than its ensemble counterparts, yet it had difficulties fitting the data in the last month of the 4-month assimilation window when the data density was significantly decreased. The authors attributed this failure to increased advective nonlinearities in the presence of an eddy shedding causing the tangent linear model (TLM) to become inaccurate. In a separate study [Ngodock, H.E., Smith, S.R., Jacobs, G.A., 2007. Cycling the representer algorithm for variational data assimilation with the Lorenz attractor. Monthly Weather Review 135 (2), 373–386] applied the cycling representer algorithm to the Lorenz attractor and demonstrated that the cycling solution was able to accurately fit the data within each cycle and beyond the range of accuracy of the TLM, once adjustments were made in the early cycles, thus overcoming the difficulties of the non-cycling solution. The cycling algorithm is used here in assimilation experiments with the nonlinear reduced gravity model. It is shown that the cycling solution overcomes the difficulties encountered by the non-cycling solution due to a limited time range of accuracy of the TLM. Thus, for variational assimilation applications where the TLM accuracy is limited in time, the cycling representer becomes a very powerful and attractive alternative, given that its computational cost is significantly lower than that of the non-cycling algorithm.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

A singular evolutive extended Kalman filter to assimilate ocean color data in a coupled physical–biochemical model of the North Atlantic ocean

Within the European DIADEM project, a data assimilation system for coupled ocean circulation and marine ecosystem models has been implemented for the North Atlantic and the Nordic Seas. One objective of this project is to demonstrate the relevance of sophisticated methods to assimilate satellite data such as altimetry, surface temperature and ocean color, into realistic ocean models. In this paper, the singular evolutive extended Kalman (SEEK) filter, which is an advanced assimilation scheme where three-dimensional, multivariate error statistics are taken into account, is used to assimilate ocean color data into the biological component of the coupled system. The marine ecosystem model, derived from the FDM model [J. Mar. Res. 48 (1990) 591], includes 11 nitrogen and carbon compartments and describes the synthesis of organic matter in the euphotic zone, its consumption by animals of upper trophic levels, and the recycling of detritic material in the deep ocean. The circulation model coupled to the ecosystem is the Miami isopycnic coordinate ocean model (MICOM), which covers the Atlantic and the Arctic Oceans with an enhanced resolution in the North Atlantic basin. The model is forced with realistic ECMWF ocean/atmosphere fluxes, which permits to resolve the seasonal variability of the circulation and mixed layer properties. In the twin assimimation experiments reported here, the predictions of the coupled model are corrected every 10 days using pseudo-measurements of surface phytoplankton as a substitute to chlorophyll concentrations measured from space. The diagnostics of these experiments indicate that the assimilation is feasible with a reduced-order Kalman filter of small rank (of order 10) as long as a sufficiently good identification of the error structure is available. In addition, the control of non-observed quantities such as zooplankton and nitrate concentrations is made possible, owing to the multivariate nature of the analysis scheme. However, a too severe truncation of the error sub-space downgrades the propagation of surface information below the mixed layer. The reduction of the actual state vector to the surface layers is therefore investigated to improve the estimation process in the perspective of sea-viewing wide field-of-view sensor (SeaWiFS) data assimilation experiments.     

Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model

In order to improve the ocean forecasting in the North Sea and Baltic Sea, an assimilation scheme based on a bottom-topography-dependent anisotropic recursive filter has been used in this study. This scheme can stretch or flatten the shape of a local representative contour surface of the background error covariance function into the form of an ellipse. Furthermore, the computing efficiency has been largely improved due to implicit computation of the background error covariance. A two-month experiment has been used for verifying the impact of assimilating ocean profile observations on ocean forecasting. The results indicate that the use of temperature and salinity profiles can largely improve the oceanic forecasting. The root mean square differences between the forecasts and observations for temperature and salinity have been reduced by 36% and 18% in the experiment period, respectively. Moreover, it is found that the anisotropic recursive filter approach is especially efficient in areas with complex coastlines and sharp fronts, e.g., inner Danish waters. The results also show that the propagation of observation information from an observation position to its neighboring grid points is closely related to currents.     

Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.     

The coupled oceanographic–tomographic analysis and prediction system: A practical means for utilizing high frequency tomography in the world's oceans

Details are presented of a methodology that utilizes acoustic travel time information in an ocean circulation model. Recent developments of this model-oriented tomography are discussed, representing some significant improvements over earlier formulations. More accurate means of determining the arrival times of specific ray paths are detailed, along with a means of estimating possible errors in the calculated travel times. The assimilation of the observed arrival time information into an ocean model is achieved using a Kalman gain, and more advanced expressions for calculating the Kalman gain are presented. A formulation to account for errors in the stated positions of a source and receiver is also presented. It is shown that the methodology performs fairly well in reproducing observed travel time anomalies. However, the model-predicted anomalies along a specific ray path may not always track the observed anomalies for that path when assimilating multiple ray path data. Results indicate that additional work is required to determine a means of handling observed arrival time data without having prior knowledge of the magnitude of errors in the observations. Results from simulation experiments provide estimates of: (1) potential errors when the travel times for ray paths are only sampled at discreet intervals as opposed to continuously and (2) to what degree acoustic data can be expected to “correct” model-predicted fields.     

The impact of bio-optical heating on the properties of the upper ocean: A sensitivity study using a 3-D circulation model for the Labrador Sea

The impact of bio-optical heating on the properties of the upper Labrador Sea water was investigated by considering changes in light attenuation in water associated with the seasonal change of chlorophyll distribution. The time- and depth-dependent attenuation coefficients were obtained from remotely sensed SeaWiFS ocean-colour data. Sea-surface temperature (SST) and mixed-layer depth (MLD) were computed from a three-dimensional ocean circulation model. The model was integrated from 1999 to 2003 with 6-hourly atmospheric forcing. The changes in SST and MLD attributable to bio-optical heating were determined by comparing the model results using the observed attenuation coefficients (chlorophyll) to those using a weak and constant attenuation (clear water). The model results show that bio-optical heating is controlled mainly by chlorophyll concentration and MLD. The increase in SST is around 1 °C in most parts of the Labrador Sea and the shelves, and up to 2.7 °C in areas of shallow MLD and high chlorophyll concentrations (the Grand Banks and Northeastern Newfoundland Shelf). The increase is much higher than that found in previous studies, which was typically a fraction of a degree. Bio-optical heating also can enhance the stratification of the upper ocean and reduce the MLD by 20–50%.     

应用CDOM光学特性估算水体COD——以辽宁省盘锦市双台子河和辽东湾为例

有色可溶性有机物(chromophoric dissolved organic matter,CDOM)是水体中一类重要的光吸收物质,在水色遥感中,其光学特性主要以440 nm处的吸收系数和光谱斜率来表征,利用这些光学特性进行水环境要素遥感反演具有较广泛的应用前景.化学需氧量(chemical oxygen deman...     

The relationship among environmental variables,jellyfish and non‐gelatinous zooplankton: A case study in the north of the Gulf of Oman

Processes underlying the temporal and spatial variations observed in the distribution of jellyfish and non‐gelatinous zooplankton in the Gulf of Oman are not well understood. This information gap is clearly a major issue in controlling the harmful blooms of jellyfish and non‐gelatinous zooplankton. Samples of jellyfish and non‐gelatinous zooplankton were collected from six stations in Chabahar Bay and three stations in Pozm Bay within four seasons. At each station, environmental variables were also recorded from bottom and surface water. A total of 83 individuals of medusae representing four species of Scyphozoa (i.e., Cyanea nozakii, Chrysaora sp., Pelagia noctiluca, Catostylus tagi) and species of Hydrozoa (i.e., Diphyes sp., Rhacostoma sp., Aequorea spp.) were observed in the study area. A total of 70,727.25 individuals/m^?3 of non‐gelatinous zooplankton dominated by copepods and cladocerans were collected in nine stations within the four seasons. The results of a RELATE analysis yielded no significant association between species composition for jellyfish and non‐gelatinous zooplankton. Among environmental variables, water transparency, nitrite concentration, water depth and temperature were better associated with the total variation in jellyfish species composition than with that of non‐gelatinous zooplankton. Dissolved oxygen, pH, and phosphate concentration were significant environmental variables associated with the variation in the spatial and temporal distribution patterns of non‐gelatinous zooplankton assemblages. Although some jellyfish species (i.e., Rhacostoma sp., Pelagia noctiluca, Catostylus tagi) occur independently of non‐gelatinous zooplankton assemblages, other jellyfish (i.e., Chrysaora sp., Aequorea spp., Cyanea nozakii, Diphyes sp.) are strongly correlated with non‐gelatinous zooplankton assemblages.     

The origin and charging directions of Neogene biodegraded oils: A geochemical study of large oil fields in the middle of the Shijiutuo Uplift,Bohai Sea,China

Two large oil fields (QHD32-6 and QHD33-1), located in the middle part of the Shijiutuo Uplift, have generally suffered mild biodegradation. Based on multivariate statistical analysis of the biomarker parameters, this study discussed the origin and charging directions for these two oil fields.In contrast to Ed₃-derived oil, all available oil samples from these two large oil fields displayed low C₁₉/C₂₃, C₂₄/C₂₆ and high G/H and 4-MSI, which are attributed to the mixtures of oils derived from the Shahejie (Es₁ and Es₃) source rocks. Oils in QHD32-6, which contain relatively more Es₃-derived oil, are called Group I oils, and most oils in QHD33-1, which share relatively more Es₁-derived oil, are called Group II oils. Our mixed oil experiments reveal the predominant Es₃- and Es₁-derived oil contribution for Group I and Group II oil groups, respectively; however, the selection of end member oils warrants further research.Based on comparisons of biomarker parameters, the QHD32-6 oil field was mainly charged in the north by oil generated from Shahejie formation source rocks in the Bozhong depression. However, oils from the north of QHD32-6 field display a remarkable difference to the oils in the south of this field, which may indicate that a charging pathway exists from the QHD33-1 field. Considering the variations in biomarker compositions in the west to -east and northwest to -southeast sections across the QHD33-1 and QHD32-6 oil fields, it can be deduced that Es₃-sourced oil migrated westward to the QHD32-6 traps, and then charging by Es₁ oil from the Bozhong Sag resulted in the QHD33-1 oil field being characterized by the mixture of Es₃- and Es₁-sourced oil. Moreover, migration of Es₁-derived oil from the Qinnan Sag was not identified, implying that the QHD33-1 oil field is mainly charged from the northeast of the Bozhong Sag.