Global Ocean Data Assimilation Experiment Research Articles

Data assimilation system for the finite-volume community ocean model based on a localized weighted ensemble Kalman filter

The unstructured-grid finite-volume community ocean model (FVCOM) offers the potential to build a high-resolution operational prediction system because it can effectively adapt to complex boundaries and flexibly perform local mesh encryption. A matching assimilation system is an indispensable part of an operational forecast system, while the existing assimilation method of the FVCOM [e.g., the ensemble Kalman filter (EnKF)] has difficulty accommodating strong nonlinear model/observation operators and non-Gaussian errors. The localized weighted EnKF (LWEnKF) is a particle filter (PF) that combines the advantages of the EnKF and the PF, which can effectively address nonlinearity and non-Gaussianity and can provide a more accurate initial field for models. An observation preprocessing system and a new data assimilation system based on the LWEnKF for the FVCOM are developed. For the preprocessing system, a K-dimensional tree-based observation thinning method is proposed that can accommodate representative observation efficiently for unstructured grids. Through a 1-month global ocean data assimilation experiment, the effect of our assimilation system is compared with the EnKF and the Mercator analysis forecast product, which comes from a reduced-order Kalman filter system. The statistical verification includes five metrics for various sea surface fields, temperature, and salinity profiles. The results show that the system performs better than the EnKF, although there is still a gap with Mercator in some aspects.

A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico

Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly in ocean regions that are characterized by rapid changes in their circulation due to changes in atmospheric forcing or due to the release of available potential energy through the development of instabilities. Ocean numerical models’ useful forecast window is no longer than two days over a given area with the best initialization possible. Predictions quickly diverge from the observational field throughout the water and become unreliable, despite the fact that they can simulate the observed dynamics through other variables such as temperature, salinity and sea surface height. Numerical methods such as harmonic analysis are used to predict both short- and long-term tidal currents with significant accuracy. However, they are limited to the areas where the tide was measured. In this study, a new approach to ocean current prediction based on deep learning is proposed. This method is evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than 4 days with a root mean square error less than 0.05 cm·s−1 and a correlation coefficient of 0.6.

Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Abstract. The quality of the upgraded version of the Copernicus Marine Environment Monitoring Service (CMEMS) biogeochemical operational system of the Mediterranean Sea (MedBFM) is assessed in terms of consistency and forecast skill, following a mixed validation protocol that exploits different reference data from satellite, oceanographic databases, Biogeochemical Argo floats, and literature. We show that the quality of the MedBFM system has been improved in the previous 10 years. We demonstrate that a set of metrics based on the GODAE (Global Ocean Data Assimilation Experiment) paradigm can be efficiently applied to validate an operational model system for biogeochemical and ecosystem forecasts. The accuracy of the CMEMS biogeochemical products for the Mediterranean Sea can be achieved from basin-wide and seasonal scales to mesoscale and weekly scales, and its level depends on the specific variable and the availability of reference data, the latter being an important prerequisite to build robust statistics. In particular, the use of the Biogeochemical Argo floats data proved to significantly enhance the validation framework of operational biogeochemical models. New skill metrics, aimed to assess key biogeochemical processes and dynamics (e.g. deep chlorophyll maximum depth, nitracline depth), can be easily implemented to routinely monitor the quality of the products and highlight possible anomalies through the comparison of near-real-time (NRT) forecasts skill with pre-operationally defined seasonal benchmarks. Feedbacks to the observing autonomous systems in terms of quality control and deployment strategy are also discussed.

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean (hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving, hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles (mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.

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.

Recent progress in performance evaluations and near real-time assessment of operational ocean products

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here.

Synthesis of new scientific challenges for GODAE OceanView

The marine environment plays an increasingly important role in shaping economies and infrastructures, and touches upon many aspects of our lives, including food supplies, energy resources, national security and recreational activities. Global Ocean Data Assimilation Experiment (GODAE) and GODAE OceanView have provided platforms for international collaboration that significantly contribute to the scientific development and increasing uptake of ocean forecasting products by end users who address societal issues such as those listed above. Many scientific challenges and opportunities remain to be tackled in the ever-changing field of operational oceanography, from the observing system to modelling, data assimilation and product dissemination. This paper provides a brief overview of past achievements in GODAE OceanView, but subsequently concentrates on the future scientific foci of GODAE OceanView and its Task Teams, and provides a vision for the future of ocean forecasting.

DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

From satellite altimetry to Argo and operational oceanography: three revolutions in oceanography

Abstract. The launch of the French/US mission Topex/Poseidon (T/P) (CNES/NASA) in August 1992 was the start of a revolution in oceanography. For the first time, a very precise altimeter system optimized for large-scale sea level and ocean circulation observations was flying. T/P alone could not observe the mesoscale circulation. In the 1990s, the ESA satellites ERS-1/2 were flying simultaneously with T/P. Together with my CLS colleagues, we demonstrated that we could use T/P as a reference mission for ERS-1/2 and bring the ERS-1/2 data to an accuracy level comparable to T/P. Near-real-time high-resolution global sea level anomaly maps were then derived. These maps have been operationally produced as part of the SSALTO/DUACS system for the last 15 yr. They are now widely used by the oceanographic community and have contributed to a much better understanding and recognition of the role and importance of mesoscale dynamics. Altimetry needs to be complemented with global in situ observations. At the end of the 90s, a major international initiative was launched to develop Argo, the global array of profiling floats. This has been an outstanding success. Argo floats now provide the most important in situ observations to monitor and understand the role of the ocean on the earth climate and for operational oceanography. This is a second revolution in oceanography. The unique capability of satellite altimetry to observe the global ocean in near-real-time at high resolution and the development of Argo were essential for the development of global operational oceanography, the third revolution in oceanography. The Global Ocean Data Assimilation Experiment (GODAE) was instrumental in the development of the required capabilities. This paper provides an historical perspective on the development of these three revolutions in oceanography which are very much interlinked. This is not an exhaustive review and I will mainly focus on the contributions we made together with many colleagues and friends.

GODAE inter-comparisons in the Tasman and Coral Seas

This paper compares the performance of operational short-range ocean forecast systems developed under the Global Ocean Data Assimilation Experiment (GODAE) – an international effort to demonstrate the feasibility of operational ocean forecasting. ‘Best estimates’ from four different operational forecast systems (either analyses, hindcasts, or nowcasts) are inter-compared for the Tasman and Coral Seas, off eastern Australia. Systems considered include those developed in Australia, France, the USA, and the UK. Each system is compared to observations of along-track sea-level anomaly, sea-surface temperature, near-surface velocity, and sub-surface temperature and salinity. All have their strengths and weaknesses, and each system out-performs all others in one aspect or another. With few exceptions, all systems demonstrate signal-to-noise ratios greater than one for all variables. Due to the Australian focus, in addition to the best estimates from the operational systems, operational forecasts and a delayed-mode reanalysis are also inter-compared using the Australian system. The Australian system generally performs the best for sea-level anomaly; the French system is best for near-surface velocities; the USA system generally performs the best for sea-surface temperature; and the UK system is best for sub-surface temperature and salinity. These findings provide useful indicators of deficiencies in each system and clear metrics by which future developments should be assessed. Based on these results and other practical considerations the adoption of multimodel consensus forecasting, using all available forecasts from all systems, is promoted as the most robust approach for the user community. Such developments are being pursued under GODAE OceanView – the successor to GODAE. The results show the success of GODAE in demonstrating the feasibility of operational oceanography.

Fifty years of numerical modeling of baroclinic ocean

This paper presents a brief critical analysis of the main historical stages of numerical modeling for the last fifty years. It was a half a century ago that the numerical simulation of an actual baroclinic ocean was initiated by the author and his students [1, 2]. In meteorology, studies on the numerical modeling of a baroclinic atmosphere existed much earlier [21, 22]. Despite this, a similar move in oceanography was met with strong resistance. At that time, there were many studies on the calculation of the total mass transport. The founders of this field, V.B. Shtokman, H. Sverdrup, and W. Munk, were mistaken in believing that they addressed baroclinic models of the ocean. The author preferred works by V. Ekman [12] and I. Sandstrom and B. Helland-Hansen [19]. A generalization of recent studies made it possible to come to some conclusions on the need to use the level of the free oceanic surface as a basis rather than the function of total mass transport, on the role of the baroclinic β effect (BARBE), on the joint effect of baroclinicity and bottom relief (JEBAR), etc. The author conditionally divides these fifty years into the following three stages. (1) The first stage was 1961–1969, when the author and his students performed almost exclusively diagnostic and adaptation calculations of climatic characteristics. (2) The second stage began with papers by K. Bryan [23] and his students. This is an important and promising stage involving mainly prognostic studies and four-dimensional analysis. The major advances in modeling at this stage (the Gulf Stream separation point [61], the Kuroshio seasonal evolution [63], the formation of the cold intermediate layer in the Black Sea [80], the subsurface countercurrent in the Caspian Sea [25], the realistic four-dimensional analysis of the Kara Sea [60], etc.) were due to high-resolution and/or data assimilation with an adequate period of integration. (3) The third stage began with the activities of international intercalibration programs such as the Arctic Ocean Model Intercomparison Project (AOMIP), the Global Ocean Data Assimilation Experiment (GODAE), Coordinated Ocean-Ice Reference Experiments (COREs), etc. Despite some defects initially, this is the most significant stage. For example, there is still very little data on GODAE, and COREs data are often used for a comparison of integral characteristics, the reliability of which cannot be established by direct measurements.

Numerical modeling with application to tracking marine debris

This paper describes different numerical models of ocean circulation the output of which can be applied to study patterns and pathways of drifting marine debris. The paper focuses on model output that is readily available rather than on numerical models that could be configured and run locally. These include operational models from the US Navy (the Navy Layered Ocean Model (NLOM), Coastal Ocean Model (NCOM), and Hybrid Coordinate Ocean Model (HYCOM)), data assimilating reanalysis models (the Simple Ocean Data Assimilation (SODA), the Global Ocean Data Assimilation Experiment (GODAE) models), and the European Center for Medium-Range Weather Forecasts (ECMWF) ocean reanalysis (Ocean Reanalysis System, ECMWF/ORA-S3). The paper describes the underlying physics in each model system, limitations, and where to obtain the model output.

A Model-Based Observation-Thinning Scheme for the Assimilation of High-Resolution SST in the Shelf and Coastal Seas around China

Abstract The use of high-density remote sensing buoys and ship-based observations play an increasingly crucial role in the operational assimilation and forecast of oceans. With the recent release of several high-resolution observation datasets, such as the Global Ocean Data Assimilation Experiment (GODAE) high-resolution SST (GHRSST) datasets, the development of observation-thinning schemes becomes important in the process of data assimilation because the huge quantity and dense spatial–temporal distributions of these datasets might make it expensive to assimilate the full dataset into ocean models or even decay the assimilation result. In this paper, an objective model simulation ensemble-based observation-thinning scheme is proposed and applied to a Chinese shelf–coastal seas eddy-resolving model. A successful thinning scheme should select a subset of observations yielding a small analysis error variance (AEV) while keeping the number of observations to as few as possible. In this study, the background error covariance (BEC) is estimated using the historical ensemble and then the subset of observations to minimize the AEV is selected, which is estimated from the Kalman theory. The authors used this method in the GHRSST product to cover the shelf and coastal seas around China and then verified the result with an estimation function and assimilation–forecast systems.

Influence of the 26°N RAPID–MOCHA Array and Florida Current Cable Observations on the ECCO–GODAE State Estimate

Abstract The incorporation of local temperature and salinity observations from the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array (RAPID–MOCHA), as well as the cable estimates of volume transport in the Florida Current (FC), is tested in the Estimating the Circulation and Climate of the Ocean–Global Ocean Data Assimilation Experiment (ECCO–GODAE) estimation system for their impact on the estimate of the meridional overturning circulation (MOC) and the meridional heat transport in the Atlantic. An experimental setup covering the first deployment period of RAPID–MOCHA from March 2004 to March 2005 is used to test different strategies for incorporating these datasets. Incorporating both monthly means of the FC data and monthly means of the RAPID–MOCHA temperature and salinity measurements at the eastern and western boundaries of the basin as an observational constraint in a 1-yr experiment results in an adjustment to the reference estimate, which does not include these datasets, of approximately 1 Sv (1 Sv ≡ 106 m3 s−1) in the MOC at 26°N and the adjacent latitudes (approximately ±15°), with a larger northward branch of the MOC above 1000 m, compensated by a larger flow in the southward branch of the MOC between approximately 2000 and 3000 m. The meridional heat transport from 26°N to near 40°N is approximately 0.05 PW larger than in the reference experiment.

Research and development of the New Generation Sea Surface Temperature for Open Ccean (NGSST-O) product and its demonstration operation

Real-time generation and distribution of the New Generation Sea Surface Temperature for Open Ocean (NGSST-O) product began in September 2003 as a demonstration operation of the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution Sea Surface Temperature Pilot Project. Satellite sea surface temperature (SST) observations from infrared radiometers (AVHRR, MODIS) and a microwave radiometer (AMSR-E) are objectively merged to generate the NGSST-O product, which is a quality-controlled, cloud-free, high-spatial-resolution (0.05° gridded), wide-coverage (13–63° N, 116–166° E), daily SST digital map. The NGSST-O demonstration operation system has been developed in cooperation with the Japanese Space Agency (JAXA) and has produced six years of continuous data without gaps. Comparison to in situ SSTs measured by drifting buoys indicates that the root mean-square error of NGSST-O has been kept at approximately 0.9°C.

GODAE APPLICATIONS USEFUL TO NAVIES THROUGHOUT THE WORLD

Abstract : Global Ocean Data Assimilation Experiment (GODAE) brought together an international group of researchers to address the problem of predicting the ocean environment. GODAE addressed the necessary technological development for data assimilation, which is a critical choke point within the process of providing meaningful information. These efforts brought a significant step forward, and today these technologies are applied operationally in areas of historically strong need. One application is for navies throughout the globe. Navies are now making regular use of oceanographic information forecast by numerical models that are initialized by assimilation of global satellite and in situ data sets. Prior to GODAE, forecast properties were not available operationally, and the information provided to navy operators was typically either climatology or local observations. The ability to forecast the ocean environment has significantly changed how navies operate. Rather than going to a location at a predetermined time and determining whether the environment is suitable to safely conduct a mission, navies can now choose where and when they may operate safely and efficiently to either avoid adverse effects or take advantage of favorable conditions. Several example events over recent years highlight how navies around the world use GODAE forecast information.

APPLICATIONS IN COASTAL MODELING AND FORECASTING

During the Global Ocean Data Assimilation Experiment (GODAE), numerical modeling and prediction in coastal and shelf seas benefited from development of state-of-the-art, data-assimilative, and data-validated large-scale models that can supply initial and boundary conditions to nested domains. Rather than attempting an exhaustive synthesis, this article illustrates the progress in coastal ocean modeling and prediction made possible by GODAE, either directly by providing estimates, or more subtly by rendering coastal forecasting more feasible and its applications more obvious.

Observing System Evaluations Using GODAE Systems

Global ocean forecast systems, developed under the Global Ocean Data Assimilation Experiment (GODAE), are a powerful means of assessing the impact of different components of the Global Ocean Observing System (GOOS). Using a range of analysis tools and approaches, GODAE systems are useful for quantifying the impact of different observation types on the quality of analyses and forecasts. This assessment includes both existing and future observation platforms. Many important conclusions can be drawn from these studies. It is clear that altimeter data are extremely important for constraining mesoscale variability in ocean forecast systems. The number of altimeters is also important. For example, near-real-time applications need data from four altimeters to achieve skill that is similar to systems using data from two altimeters in delayed mode. Another important result is that sea surface temperature is the only observation parameter that adequately monitors ocean properties in coastal regions and shallow seas. Assimilation of Argo data provides a significant, measurable improvement to GODAE systems, and is the only observation platform that provides global-scale information for constraining salinity. The complementary nature of different components of GOOS is now clear and the emergence of new assimilation techniques for observing system evaluation provides the GODAE community with a practical path toward routine GOOS monitoring.

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.

DATA ASSEMBLY AND PROCESSING FOR OPERATIONAL OCEANOGRAPHY: TEN YEARS OF ACHIEVEMENTS

Data assembly and processing centers are essential elements of the operational oceanography infrastructure. They provide data and products needed by modeling and data assimilation systems; they also provide products directly usable for applications. This paper discusses the role and functions of the data centers for operational oceanography. It describes some of the main data assembly centers (Argo and in situ data, altimetry, sea surface temperature) developed during the Global Ocean Data Assimilation Experiment. An overview of other data centers (wind and fluxes, ocean color, sea ice) is also given. Much progress has been achieved over the past 10 years to validate, intercalibrate, and merge altimeter data from multiple satellites. Accuracy and timeliness of products have been improved, and new products have been developed. The same is true for sea surface temperature data through the Global High-Resolution Sea Surface Temperature Pilot Project. A breakthrough in processing, quality control, and assembly for in situ data has also been achieved through the development of the real-time and delayed-mode Argo data system. In situ and remote-sensing data are now systematically and jointly used to calibrate, validate, and monitor over the long term the quality and consistency of the global ocean observing system. Main results are illustrated. There is also a review of the development and use of products that merge in situ and remote-sensing data. Future issues and main prospects are discussed in the conclusion.