scholarly journals Transports and budgets in a 1/4° global ocean reanalysis 1989–2010

2012 ◽  
Vol 9 (1) ◽  
pp. 261-290 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are vital to understanding the role of the oceans in climate and climate change. In contrast atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy permitting resolutions of 1/4° or better, along with new global ocean observation programs, now make a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent ocean and atmospheric estimates.

scholarly journals Transports and budgets in a 1/4 ° global ocean reanalysis 1989–2010

Ocean Science ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 333-344 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large-scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are important to understanding the role of the oceans in climate and climate change. In contrast, atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy-permitting resolutions of 1/4 ° or better, along with new global ocean observation programs, now makes a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent oceanic and atmospheric estimates.

scholarly journals DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR OPERATIONAL OCEANOGRAPHY AND OCEAN FORECASTING

2013 ◽  
Vol 31 (2) ◽  
pp. 210 ◽  
Author(s):  
Jose Antonio Moreira Lima ◽  
Renato Parkinson Martins ◽  
Clemente Augusto Souza Tanajura ◽  
Afonso De Moraes Paiva ◽  
Mauro Cirano Cirano ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atlantic Ocean ◽  
Large Scale ◽  
Integrated Approach ◽  
Global Ocean ◽  
Assimilation Experiment ◽  
In Situ Data ◽  
Ocean Data Assimilation ◽  
Observation Network ◽  
Ocean Forecasting

ABSTRACT. This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network (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. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in situ data such as the South Atlantic Ocean. An integrated approach is proposed here in which the large-scale circulation in the Atlantic Ocean is modeled in a first step, and gradually downscaled into higher resolution regional models. This approach is able to resolve important processes such as the Brazil Current and associated meso-scale 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), in which REMO takes part.Keywords: ocean models, ocean measurements, data assimilation. RESUMO. Este artigo apresenta o planejamento, implementação e alguns resultados da Rede de Modelagem e Observação Oceanográfica, com acrônimo REMO, para águas territoriais brasileiras. A previsão de condições oceânicas tem sido um importante tópico de pesquisa científica ao longo da última década, devido a estudos relacionados com mudanças climáticas assim como interesse por previsões sinóticas de curto prazo de variáveis tais como correntes marinhas e temperatura da água. É um desafio realizar o projeto de um sistema de previsão para uma região oceânica com baixa disponibilidade de medições, como o Oceano Atlântico Sul. Uma proposta de desenvolvimento integrado é apresentada neste trabalho, onde um modelo de circulação oceânica de todo Oceano Atlântico foi implementado como passo inicial, e gradualmente foram aninhados modelos regionais com maior resolução espacial. Este artigo apresenta a estratégia de desenvolvimento destes modelos oceânicos utilizando o conhecimento científico disponibilizado por pesquisadores de uma rede de instituições brasileiras, com eventual colaboração de pesquisadores internacionais. Esta iniciativa brasileira possui pontos comuns com um projeto de cooperação científica internacional, denominado Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView), da qual a REMO faz parte.Palavras-chave: oceanografia operacional, modelagem oceânica, sensoriamento remoto, medições oceanográficas, assimilação de dados.

Evaluation of Global Ocean Data Assimilation Experiment products on South Florida nested simulations with the Hybrid Coordinate Ocean Model

2008 ◽  
Vol 59 (1) ◽  
pp. 47-66 ◽  
Author(s):  
Vassiliki H. Kourafalou ◽  
Ge Peng ◽  
HeeSook Kang ◽  
Patrick J. Hogan ◽  
Ole-Martin Smedstad ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
South Florida ◽  
Global Ocean ◽  
Assimilation Experiment ◽  
Ocean Data Assimilation ◽  
Hybrid Coordinate Ocean Model

scholarly journals A Multiscale Approach to High-Resolution Ocean Profile Observations within a 4DVAR Analysis System

2019 ◽  
Vol 147 (2) ◽  
pp. 627-643 ◽  
Author(s):  
Matthew J. Carrier ◽  
John J. Osborne ◽  
Hans E. Ngodock ◽  
Scott R. Smith ◽  
Innocent Souopgui ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Multiscale Approach ◽  
Background Error ◽  
Underwater Gliders ◽  
Ocean Data Assimilation ◽  
Error Correlation ◽  
Coastal Ocean Model ◽  
Analysis System

Abstract Most ocean data assimilation systems are tuned to process and assimilate observations to constrain features on the order of the mesoscale and larger. Typically this involves removal of observations or computing averaged observations. This procedure, while necessary, eliminates many observations from the analysis step and can reduce the overall effectiveness of a particular observing platform. Simply including these observations is not an option as doing so can produce an overdetermined, ill-conditioned problem that is more difficult to solve. An approach, presented here, aims to avoid such issues while at the same time increasing the number of observations within the assimilation. A two-step assimilation procedure with the four-dimensional variational data assimilation (4DVAR) system is adopted. The first step attempts to constrain the large-scale features by assimilating a set of super observations with appropriate background error correlation scales and error variances. The second step then attempts to correct smaller-scale features by assimilating the full observation set with shorter background error correlation scales and appropriate error variances; here the background state is taken as the analysis from the first step. Results using a real high-density observation set from underwater gliders in the region southeast of Iceland, collected during the 2017 Nordic Recognized Environmental Picture (NREP) experiment, will be shown using the Navy Coastal Ocean Model 4DVAR (NCOM-4DVAR).

scholarly journals GODAE: The Global Ocean Data Assimilation Experiment

Oceanography ◽  
2009 ◽  
Vol 22 (3) ◽  
pp. 14-21 ◽  
Author(s):  
Michael Bell ◽  
Michel Lefèbvre ◽  
Pierre-Yves Le Traon ◽  
Neville Smith ◽  
Kirsten Wilmer-Becker
Keyword(s):  
Data Assimilation ◽  
Global Ocean ◽  
Assimilation Experiment ◽  
Ocean Data Assimilation

scholarly journals Variational data assimilation with superparameterization

2015 ◽  
Vol 2 (2) ◽  
pp. 513-536 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Climate Models ◽  
Low Cost ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Small Scale ◽  
New Applications ◽  
Lorenz 96

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

Evaluating Effects of Observational Data Assimilation in General Ocean Circulation Model by Ensemble Kalman Filtering: Numerical Experiments with Synthetic Observations

2021 ◽  
pp. 50-66
Author(s):  
V. N. Stepanov ◽  
◽  
Yu. D. Resnyanskii ◽  
B. S. Strukov ◽  
A. A. Zelen’ko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Ocean Circulation ◽  
Numerical Experiments ◽  
Turbulent Viscosity ◽  
Bottom Topography ◽  
Synthetic Data ◽  
Circulation Model ◽  
Ocean Model ◽  
Global Ocean

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure.

Sign in / Sign up

Export Citation Format

Share Document