Ocean Prediction System Research Articles

Impact of SARAL/AltiKa-Derived Sea Level Anomaly in a Data Assimilative Ocean Prediction System for the Indian Ocean

The impact of SARAL/AltiKa derived sea level anomaly (SLA) has been studied by assimilating it along with Jason-2 and Cryosat-2 SLA in the Princeton Ocean model (POM) using ensemble optimal interpolation (EnOI) technique. For isolating the extra benefit brought by SARAKL/Altika, a parallel run with assimilation of only Jason-2 and Cryosat-2 SLA has also been conducted. The importance of SARAL SLA in a data assimilative ocean prediction system has been evaluated with special emphasis on the improvement in thermocline depth, depth of the 20° isotherm, subsurface temperature and currents. Comparison with RAMA buoy has shown a positive impact of up to 13% for 20°C isotherm and up to 17% for thermocline depth after assimilating SARAL SLA. An overall improvement in temperature profile is also observed when compared with analogous profiles from RAMA buoys and Argo floats. Improvement in zonal currents away from the equator has also been noticed.

Status and future of global and regional ocean prediction systems

Operational evolution of global and regional ocean forecasting systems has been extremely significant in recent years. Global Ocean Data Assimilation Experiment (GODAE) Oceanview supports the national research groups providing them with coordination and sharing expertise among the partners. Several systems have been set up and developed pre-operationally, and the majority of these are now fully operational; at the present time, they provide medium- and long-term forecasts of the most relevant ocean physical variables. These systems are based on ocean general circulation models and data-assimilation techniques that are able to correct the model with the information inferred from different types of observations. A few systems also incorporate a biogeochemical component coupled with the physical system, while others are based on coupled ocean–wave–ice–atmosphere models. The products are routinely validated with observations in order to assess their quality. Data and product implementation and organization, as well as service, for users have been well tried and tested, and most of the products are now available to users. The interaction with different users is an important factor in the development process. This paper provides a synthetic overview of the GODAE OceanView prediction systems.

Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10‐day forecasts on a 1/4°‐resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea‐level anomaly and sea‐surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D‐Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis‐based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium‐range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open‐water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration

In recent years, the demand for improved environmental forecasts in the Arctic has intensified as maritime transport and offshore exploration increase. As a result, Canada has accepted responsibility for the preparation and issuing services for the new Arctic MET/NAV Areas XVII and XVIII. Environmental forecasts are being developed based on a new integrated Arctic marine prediction system. Here, we present the first phase of this initiative, a short‐term pan‐Arctic 1/12° resolution Regional Ice Prediction System (RIPS). RIPS is currently set to perform four 48 h forecasts per day. The RIPS forecast model (CICE 4.0) is forced by atmospheric forecasts from the Environment Canada regional deterministic prediction system. It is initialized with a 3D‐Var analysis of sea ice concentration and the ice velocity field and thickness distribution from the previous forecast. The other forcing (surface current) and initialization fields (mixed‐layer depth, sea surface temperature and salinity) come from the 1/4° resolution Global Ice Ocean Prediction System. Three verification methods for sea ice concentration are presented. Overall, verifications over a complete seasonal cycle (2011) against the Ice Mapping System ice extent product show that RIPS 48 h forecasts are better than persistence during the growth season while they have a lower skill than persistence during the melt period. A better representation of landfast ice, oceanic processes (wave–ice interactions, upwelling events, etc.) in the marginal ice zone and better initializing fields should lead to improved forecasts.

Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems.

A relocatable ocean model in support of environmental emergencies

During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive Relocatable Nested Ocean Model (IRENOM), based on the Harvard Ocean Prediction System (HOPS), for the Costa Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, and to the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.

The Role of Air–Sea Coupling in Seasonal Prediction of Asia–Pacific Summer Monsoon Rainfall

Abstract This study examines the role of the air–sea coupled process in the seasonal predictability of Asia–Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST–rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia–Pacific region, it is necessary to use a coupled atmosphere–ocean (tier 1) prediction system.

ATOP - The Advanced Taiwan Ocean Prediction System Based on the mpiPOM. Part 1: Model Descriptions, Analyses and Results

A data-assimilated Taiwan Ocean Prediction (ATOP) system is being developed at the National Central University, Taiwan. The model simulates sea-surface height, three-dimensional currents, temperature and salinity and turbulent mixing. The model has options for tracer and particle-tracking algorithms, as well as for wave-induced Stokes drift and wave-enhanced mixing and bottom drag. Two different forecast domains have been tested: a large-grid domain that encompasses the entire North Pacific Ocean at 0.1° × 0.1° horizontal resolution and 41 vertical sigma levels, and a smaller western North Pacific domain which at present also has the same horizontal resolution. In both domains, 25-year spin-up runs from 1988 - 2011 were first conducted, forced by six-hourly Cross-Calibrated Multi-Platform (CCMP) and NCEP reanalysis Global Forecast System (GSF) winds. The results are then used as initial conditions to conduct ocean analyses from January 2012 through February 2012, when updated hindcasts and real-time forecasts begin using the GFS winds. This paper describes the ATOP system and compares the forecast results against satellite altimetry data for assessing model skills. The model results are also shown to compare well with observations of (i) the Kuroshio intrusion in the northern South China Sea, and (ii) subtropical counter current. Review and comparison with other models in the literature of ”(i)” are also given.

Multiscale forecasting in the western North Atlantic: Sensitivity of model forecast skill to glider data assimilation

A recently implemented real-time ocean prediction system for the western North Atlantic based on the physical circulation model component of the Harvard Ocean Prediction System (HOPS) was used during an observation simulation experiment (OSE) in November 2009. The modeling system was built to capture the mesoscale dynamics of the Gulf Stream (GS), its meanders and rings, and its interaction with the shelf circulation. To accomplish this, the multiscale velocity-based feature models for the GS region are melded with the water-mass-based feature model for the Gulf of Maine and shelf climatology across the shelf/slope front for synoptic initialization. The feature-based initialization scheme was utilized for 4 short-term forecasts of varying lengths during the first two weeks of November 2009 in an ensemble mode with other forecasts to guide glider control.A reanalysis was then carried out by sequentially assimilating the data from three gliders (RU05, RU21 and RU23) for the two-week period. This two-week-long reanalysis framework was used to (i) study model sensitivity to SST and glider data assimilation; and (ii) analyze the impact of assimilation in space and time with patchy glider data. The temporal decay of salinity assimilation is found to be different than that of temperature. The spatial footprint of assimilated temperature appears to be more defined than that of salinity. A strategy for assimilating temperature and salinity in an SST-glider phased manner is then offered. The reanalysis results point to a number of new research directions for future sensitivity and quantitative studies in modeling and data assimilation.

An operational ocean circulation prediction system for the western North Atlantic: Hindcasting during July–September of 2006

A real-time ocean prediction system for the western North Atlantic (WNA) has been implemented operationally for the Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). Based on the physical circulation model component of the Harvard Ocean Prediction System (HOPS), this system is built to capture the mesoscale dynamics of the Gulf Stream (GS), its meanders and rings, and its interaction with the shelf circulation, the mid-Atlantic shelf (MAS), and the buoyancy-driven circulation in the Gulf of Maine (GOM). To accomplish this, the multiscale velocity-based feature models for the GS region are melded with the water-mass-based feature model for the GOM and with shelf climatology across the shelf-slope front for a multiscale synoptic initialization. A ring and front analysis is fed into the feature modeling scheme for generating a three-dimensional initial field. The initialization field is dynamically adjusted with wind-forcing and used in an Sea Surface Temperature (SST)-assimilative forecast mode. For this study, 8 weekly forecasts are compared with GS paths and drifter trajectories during the 2006 Shallow Water Experiment (SW06) conducted during the months of July and September. GS path and ring formation/absorption events were compared and presented by Taylor diagrams (TD). For all of 8 weeks, the forecast skills were comparable or higher than that of the persistence. The model shows reasonable skill in simulating drifter trajectories, especially over days 1–3 of simulation.

Oceanographic and atmospheric conditions on the continental shelf north of the Monterey Bay during August 2006

A comprehensive data set from the ocean and atmosphere was obtained just north of the Monterey Bay as part of the Monterey Bay 2006 (MB06) field experiment. The wind stress, heat fluxes, and sea surface temperature were sampled by the Naval Postgraduate School's TWIN OTTER research aircraft. In situ data were collected using ships, moorings, gliders and AUVs. Four data-assimilating numerical models were additionally run, including the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS ®) model for the atmosphere and the Harvard Ocean Prediction System (HOPS), the Regional Ocean Modeling System (ROMS), and the Navy Coastal Ocean Model (NCOM) for the ocean. The scientific focus of the Adaptive Sampling and Prediction Experiment (ASAP) was on the upwelling/relaxation cycle and the resulting three-dimensional coastal circulation near a coastal promontory, in this case Point Año Nuevo, CA. The emphasis of this study is on the circulation over the continental shelf as estimated from the wind forcing, two ADCP moorings, and model outputs. The wind stress during August 2006 consisted of 3–10 day upwelling favorable events separated by brief 1–3 day relaxations. During the first two weeks there was some correlation between local winds and currents and the three models’ capability to reproduce the events. During the last two weeks, largely equatorward surface wind stress forced the sea surface and barotropic poleward flow occurred over the shelf, reducing model skill at predicting the circulation. The poleward flow was apparently remotely forced by mesoscale eddies and alongshore pressure gradients, which were not well simulated by the models. The small, high-resolution model domains were highly reliant on correct open boundary conditions to drive these larger-scale poleward flows. Multiply-nested models were no more effective than well-initialized local models in this respect.

Evaluating the Impact of the Number of Satellite Altimeters Used in an Assimilative Ocean Prediction System

Abstract The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001–03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1° radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods.

High-Resolution Global and Basin-Scale Ocean Analyses and Forecasts

Abstract : The feasibility of global ocean weather prediction was just emerging as the Global Ocean Data Assimilation Experiment (GODAE) began in 1997. Ocean weather includes phenomena such as meandering currents and fronts, the surface mixed layer and sea surface temperature (SST), waves, upwelling of cold water, all influencing ocean variables such as temperature (T), salinity (S), currents, and sea surface height (SSH). Adequate real-time data input, computing power, numerical ocean models, data assimilation capabilities, atmospheric forcing, and bathymetric/boundary constraints are essential to make such prediction possible. The ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving, not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse, downward projection of surface data is a key challenge in ocean data assimilation. The need for accurate prediction of ocean features that are inadequately observed places a major burden on the ocean model, data assimilation, and atmospheric forcing. The sensitivity of ocean phenomena to atmospheric forcing and the time scale for response affect the oceanic data requirements and prediction system design. Outside of surface boundary layers and shallow regions, forecast skill is about one month globally and over many subregions, and is only modestly reduced by using climatological forcing. In addition, global ocean prediction systems must demonstrate the ability to provide initial and boundary conditions to nested regional and coastal models that enhance their predictive skill.

US GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM)

Abstract : The main objective is to use the HYbrid Coordinate Ocean Model (HYCOM) with data assimilation in an eddy-resolving, fully global ocean prediction system with transition to the Naval Oceanographic Office (NAVOCEANO) at .08 deg equatorial (~7 km mid-latitude) resolution in 2007 and .04 deg resolution by 2011. The model will include shallow water to a minimum depth of 5 m and provide boundary conditions to finer resolution coastal and regional models that may use HYCOM or a different model. In addition, HYCOM will be coupled to atmospheric, ice and bio-chemical models, with transition to the Fleet Numerical Meteorology and Oceanography Center (FNMOC) for the coupled ocean-atmosphere prediction. Basin-scale configurations will also form the backbone of the NOAA/NCEP/MMAB Ocean Forecast System. All the systems will be transitioned with assimilation of sea surface height (SSH) from satellite altimeters, sea surface temperature (SST) and temperature (T)/salinity (S) profiles, including profiles from ARGO floats. In addition, 30-day forecasts are planned once a week. The global system will include two-way coupling to an ice model and a version with two-way coupling to an atmospheric model for transition to FNMOC. The project will ensure that an accurate and generalized ocean model nesting capability is in place to support regional and littoral applications when global HYCOM becomes operational. This will include the capability to provide boundary conditions to nested models with fixed depth z-level coordinates, terrain following coordinates, generalized coordinates (HYCOM), and unstructured grids. To facilitate this goal, HYCOM will be developed into a full-featured coastal ocean model in collaboration with a partnering project. The project will participate in the multinational Global Ocean Data Assimilation Experiment (GODAE) and international GODAE-related ocean prediction system intercomparison projects.

Operational Wind Wave Prediction System at KMA

An overview of the current operational wind wave prediction system and of new developments at the Korea Meteorological Administration (KMA) is presented. KMA has operated a numerical ocean wave prediction system since 1992. The first major upgrade was done in 1999 with the adaptation of a third-generation wave model (WAM) for both regional (ReWAM-0.25 deg resolution, covering Northeast Asia) and global (GoWAM-1.25 deg resolution) domains. In 2005, the KMA replaced its NEC SX5 computer with a 1024-CPU Cray X1E system, a Parallel Vector Processor (PVP) machine with 128 node modules. A coastal ocean wave prediction system (CoWAM) has been designed and is currently under test mode. Six CoWAM domains, each of 3 deg longitude by 2 deg latitude in size, with a mesh size of 1 km, are nested inside the regional ocean wave prediction system. The directional wave spectra at the boundaries of the CoWAM are provided from a 1/12° resolution upgraded version of the operational ReWAM. The WAVEWATCH-III code (developed at NOAA) is used for the upgraded ReWAM and new CoWAM system. To ensure the required model performance in the Massively Parallel Processor (MPP) architecture of the new supercomputer, a Message Passing Interface (MPI) has been implemented in the model source code. The sea surface wind and significant wave height are verified routinely on a monthly basis. The global moored buoy data (including the coastal buoys operated by KMA) and remote sensing data from Topex/Poseidon, and Jason (retrieved wave heights) and QuikSCAT (retrieved wind data) are used for verification of the wave prediction system. This validation and a comparison of results from the new ReWAM and CoWAM against buoy data off the coast of Korea are discussed.

Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay

Abstract The Autonomous Ocean Sampling Network Phase Two (AOSN-II) experiment was conducted in and offshore from the Monterey Bay on the central California coast during July 23–September 6, 2003. The objective of the experiment was to learn how to apply new tools, technologies, and analysis techniques to adaptively sample the coastal ocean in a manner demonstrably superior to traditional methodologies, and to use the information gathered to improve predictive skill for quantities of interest to end-users. The scientific goal was to study the upwelling/relaxation cycle near an open coastal bay in an eastern boundary current region, particularly as it developed and spread from a coastal headland. The suite of observational tools used included a low-flying aircraft, a fleet of underwater gliders, including several under adaptive autonomous control, and propeller-driven AUVs in addition to moorings, ships, and other more traditional hardware. The data were delivered in real time and assimilated into the Harvard Ocean Prediction System (HOPS), the Navy Coastal Ocean Model (NCOM), and the Jet Propulsion Laboratory implementation of the Regional Ocean Modeling System (JPL/ROMS). Two upwelling events and one relaxation event were sampled during the experiment. The upwelling in both cases began when a pool of cold water less than 13 °C appeared near Cape Ano Nuevo and subsequently spread offshore and southward across the bay as the equatorward wind stress continued. The primary difference between the events was that the first event spread offshore and southward, while the second event spread only southward and not offshore. The difference is attributed to the position and strength of meanders and eddies of the California Current System offshore, which blocked or steered the cold upwelled water. The space and time scales of the mesoscale variability were much shorter than have been previously observed in deep-water eddies offshore. Additional process studies are needed to elucidate the dynamics of the flow.

Forecasting and reanalysis in the Monterey Bay/California Current region for the Autonomous Ocean Sampling Network-II experiment

Abstract During the August–September 2003 Autonomous Ocean Sampling Network-II experiment, the Harvard Ocean Prediction System (HOPS) and Error Subspace Statistical Estimation (ESSE) system were utilized in real-time to forecast physical fields and uncertainties, assimilate various ocean measurements (CTD, AUVs, gliders and SST data), provide suggestions for adaptive sampling, and guide dynamical investigations. The qualitative evaluations of the forecasts showed that many of the surface ocean features were predicted, but that their detailed positions and shapes were less accurate. The root-mean-square errors of the real-time forecasts showed that the forecasts had skill out to two days. Mean one-day forecast temperature RMS error was 0.26 ∘ C less than persistence RMS error. Mean two-day forecast temperature RMS error was 0.13 ∘ C less than persistence RMS error. Mean one- or two-day salinity RMS error was 0.036 PSU less than persistence RMS error. The real-time skill in the surface was found to be greater than the skill at depth. Pattern correlation coefficient comparisons showed, on average, greater skill than the RMS errors. For simulations lasting 10 or more days, uncertainties in the boundaries could lead to errors in the Monterey Bay region. Following the real-time experiment, a reanalysis was performed in which improvements were made in the selection of model parameters and in the open-boundary conditions. The result of the reanalysis was improved long-term stability of the simulations and improved quantitative skill, especially the skill in the main thermocline (RMS simulation error 1 ∘ C less than persistence RMS error out to five days). This allowed for an improved description of the ocean features. During the experiment there were two-week to 10-day long upwelling events. Two types of upwelling events were observed: one with plumes extending westward at point Ano Nuevo (AN) and Point Sur (PS); the other with a thinner band of upwelled water parallel to the coast and across Monterey Bay. During strong upwelling events the flows in the upper 10–20 m had scales similar to atmospheric scales. During relaxation, kinetic energy becomes available and leads to the development of mesoscale features. At 100–300 m depths, broad northward flows were observed, sometimes with a coastal branch following topographic features. An anticyclone was often observed in the subsurface fields in the mouth of Monterey Bay.

Improving the Ocean Initialization of Coupled Hurricane–Ocean Models Using Feature-Based Data Assimilation

Abstract Coupled hurricane–ocean forecast models require proper initialization of the ocean thermal structure. Here, a new feature-based (F-B) ocean initialization procedure in the GFDL/University of Rhode Island (URI) coupled hurricane prediction system is presented to account for spatial and temporal variability of mesoscale oceanic features in the Gulf of Mexico, including the Loop Current (LC), Loop Current eddies [i.e., warm-core rings (WCRs)], and cold-core rings (CCRs). Using only near-real-time satellite altimetry for the “SHA-assimilated” case, the LC, a single WCR, and a single CCR are assimilated into NAVOCEANO’s Global Digitized Environmental Model (GDEM) ocean temperature and salinity climatology along with satellite-derived daily sea surface temperature (SST) data from 15 September 2005 to produce a more realistic three-dimensional temperature field valid on the model initialization date (15 September 2005). For the “fully assimilated” case, both near-real-time altimetry and real-time in situ airborne XBT (AXBT) temperature profiles are assimilated into GDEM along with SST to produce the three-dimensional temperature field. Vertical profiles from the resulting SHA-assimilated and fully assimilated temperature fields are compared to 18 real-time AXBT temperature profiles, the ocean climatology (GDEM), and an alternative data-assimilated product [the daily North and Equatorial Atlantic Ocean Prediction System Best Estimate (RSMAS HYCOM), which uses an Optimal Interpolation (OI) based assimilation technique] to determine the relative accuracy of the F-B initialization procedure presented here. Also, the tropical cyclone heat potential (TCHP) from each of these profiles is calculated by integrating the oceanic heat content from the surface to the depth of the 26°C isotherm. Assuming the AXBT profiles are truth, the TCHP rms error for the F-B SHA-assimilated case, the F-B fully assimilated case, the GDEM ocean climatology, and the RSMAS HYCOM product is 12, 10, 45, and 26 kJ cm−2, respectively.

부산항 실시간 해양예보시스템 구축을 위한 기초연구

근래 컴퓨터 수치모델이 급속하게 발전함으로써 다양한 분야에서 수치예보기술이 응용되고 있다. 실시간 해양예보시스템도 그 중의 하나로 관측시스템, 정보제공시스템 및 모델링 시스템을 결합하여 실시간 해양정보를 제공함으로써 해상교통의 안전과 연안환경의 보호에 기여하고 있다. 본 연구에서는 부산항 실시간 해양예보시스템의 구축을 위한 기초연구로서 모델링 시스템을 개발하였으며 그 결과를 제시하고 있다. 현재 기존의 관측자료가 부족하여 M2조석의 모델링만으로 시스템의 테스트를 행하였으나 앞으로 실시간 관측자료가 충분히 구비되면 전면적인 시스템의 테스트가 필요하다. 아울러 추후 관측시스템과 정보제공시스템이 계속 구축될 예정이며 이들 시스템이 모두 구비되고 나면 인터넷을 통한 실시간 정보제공이 이루어질 수 있을 것이다. Recently the numerical prediction technique is applied to many fields, because numerical models are developed so much for last decades. The real-time ocean prediction system is one of them and is capable of providing the real-time marine information for users to promote the safety af maritime traffic and preservation of marine resources. The system is composed of observing system, data distribution system and modelling system. In this study authors develop the modelling system and show the results as preliminary study for establishing the real-time ocean prediction system in Busan port. The system test is performed only for M2 tidal modelling due to the lack qf observation data, thus a full-scale test is required in future if enough data are provided Also observing system and data distribution system will be constructed continuously in future, then service for real-time data for users will be initiated.

Data assimilation of simulated SSS SMOS products in an ocean forecasting system

Sea Surface Salinity (SSS) measured from the future Soil Moisture and Ocean Salinity (SMOS) satellite has to be considered as a new type of data. This paper addresses the impact of assimilating two different simulated SSS data types (raw track observations versus a gridded processed product) in an ocean forecasting system through an Observing System Simulation Experiment (OSSE). The OSSE consists of hindcast experiments assimilating an operational dataset of Sea Surface Temperature (SST), in-situ profiles of temperature and salinity and Sea Level Anomalies (SLA) plus various simulated SMOS SSS data. These assimilation experiments use an eddy permitting model (1/38) covering the North Atlantic from 208S to 708N and a multivariate assimilation system referred to as SAM2v1. This assimilation scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the forecast error covariance. The OSSE enables an illustration of the impact of SSS assimilation on a Mercator Ocean regional forecasting system. Several conclusions can be highlighted, such as the importance of the consistency of space/time scales between the data products and the ocean prediction systems used. This study should be viewed as a preliminary step for assimilation of SSS measured from space in an ocean forecasting system