Estimating Forecast Error Covariances for Strongly Coupled Atmosphere–Ocean 4D-Var Data Assimilation

Strongly coupled data assimilation emulates the real-world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere–ocean state. A significant challenge in strongly coupled variational atmosphere–ocean data assimilation is a priori specification of the cross covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air–sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here, the nature and structure of atmosphere–ocean forecast error cross correlations are investigated using an idealized strongly coupled single-column atmosphere–ocean 4D-Var assimilation system. Results are presented from a set of identical twin–type experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere–ocean error cross correlations. The results show significant variation in the strength and structure of cross correlations in the atmosphere–ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere–ocean correlations for different seasons and points in the diurnal cycle.

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A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

Advances in Meteorology ◽

10.1155/2017/9315601 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13

Author(s):

Hongli Fu ◽

Jinkun Yang ◽

Wei Li ◽

Xinrong Wu ◽

Guijun Han ◽

...

Keyword(s):

Data Assimilation ◽

Density Gradient ◽

Initial Conditions ◽

Ocean Model ◽

Potential Density ◽

3 Dimensional ◽

Ocean Data Assimilation ◽

Variational Framework ◽

Analysis Scheme ◽

Density Surface

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions

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How essential are Argo observations to constrain a global ocean data assimilation system?

Ocean Science Discussions ◽

10.5194/osd-12-1145-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 1145-1186 ◽

Cited By ~ 2

Author(s):

V. Turpin ◽

E. Remy ◽

P. Y. Le Traon

Keyword(s):

Data Assimilation ◽

Forecast Error ◽

Global Ocean ◽

Altimeter Data ◽

Western Boundary ◽

Argo Data ◽

Ocean Data Assimilation ◽

In Situ Observations ◽

The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

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Air-sea strongly coupled data assimilation for tropical cyclone prediction

10.5194/egusphere-egu21-3316 ◽

2021 ◽

Author(s):

Xingchao Chen

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Forecast Error ◽

Regional Scale ◽

Forecast Errors ◽

State Variables ◽

Strongly Coupled ◽

Weakly Coupled ◽

Coupled Data Assimilation ◽

Tc Prediction

<p>Air-sea interactions are critical to tropical cyclone (TC) energetics. However, oceanic state variables are still poorly initialized, and are inconsistent with atmospheric initial fields in most operational coupled TC forecast models. In this study, we first investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018) using a 200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model. Meaningful and dynamically consistent cross domain ensemble error correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update model state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation (DA). A regional-scale strongly coupled DA system based on the ensemble Kalman filter (EnKF) is then developed for TC prediction. The potential impacts of different atmospheric and oceanic observations on TC analysis and prediction are examined through observing system simulation experiments (OSSEs) of Hurricane Florence (2018). Results show that strongly coupled DA resulted in better analysis and forecast of both the oceanic and atmospheric variables than weakly coupled DA. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, strongly coupled DA reduces the forecast errors of TC track and intensity. Results show promise in potential further improvement in TC prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based strongly coupled DA system.</p>

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Dynamical Structures of Cross-Domain Forecast Error Covariance of a Simulated Tropical Cyclone in a Convection-Permitting Coupled Atmosphere–Ocean Model

Monthly Weather Review ◽

10.1175/mwr-d-20-0116.1 ◽

2021 ◽

Vol 149 (1) ◽

pp. 41-63

Author(s):

Xingchao Chen ◽

Robert G. Nystrom ◽

Christopher A. Davis ◽

Colin M. Zarzycki

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Forecast Error ◽

State Variables ◽

Ensemble Spread ◽

Strongly Coupled ◽

Error Covariance ◽

Cross Domain ◽

Forecast Lead Time ◽

Coupled Data Assimilation

AbstractUnderstanding the dynamics of the flow-dependent forecast error covariance across the air–sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air–sea interactions. A 200-member ensemble of convection-permitting forecasts from a coupled atmosphere–ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air–sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean–atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60–80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.

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Dense CTD survey versus glider fleet sampling: comparison of the performance for regional ocean prediction West of Sardinia

10.5194/os-2018-38 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jaime Hernandez-Lasheras ◽

Baptiste Mourre

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Forecast Error ◽

Model Error ◽

Ocean Model ◽

Optimal Interpolation ◽

Trial Duration ◽

Sea Trial ◽

Underwater Gliders ◽

Sea Level Anomalies

Abstract. The REP14-MEDsea trial carried out off the West coast of Sardinia in June 2014 provided a rich set of observations from both ship-based CTDs and a fleet of underwater gliders. We present the results of several simulations assimilating data either from CTDs or from different subsets of glider data, including up to 8 vehicles, in addition to satellite sea level anomalies, surface temperature and Argo profiles. The WMOP regional ocean model is used with a Local Mutimodel Ensemble Optimal Interpolation scheme to recursively ingest both lower-resolution large scale and dense local observations over the whole sea trial duration. Results show the capacity of the system to ingest both type of data, leading to improvements in the representation of all assimilated variables. These improvements persist during the 3-day periods separating two analysis. At the same time, the system presents some limitations in properly representing the smaller scale structures, which are smoothed out by the model error covariances provided by the ensemble. An evaluation of the forecasts using independent measurements from shipborne CTDs and a towed Scanfish deployed at the end of the sea trial shows that the simulations assimilating initial CTD data reduce the error by 30 to 40 % (according to the variable under consideration) with respect to the simulation without data assimilation. In the glider-data-assimilative experiments, the forecast error is reduced as the number of vehicles increases. The simulation assimilating CTDs outperforms the simulations assimilating data from one to four gliders. A fleet of eight gliders provides a similar performance as the 10-km spaced CTD initilization survey in these experiments, with an overall 40 % model error reduction capacity with respect to the simulation without data assimilation.

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OCEAN FORECASTS IN THE SOUTHWESTERN ATLANTIC: IMPACT OF DIFFERENT SOURCES OF SEA SURFACE HEIGHT IN DATA ASSIMILATION

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v31i2.292 ◽

2013 ◽

Vol 31 (2) ◽

pp. 243 ◽

Cited By ~ 2

Author(s):

Raquel Leite Mello ◽

Ana Cristina Neves de Freitas ◽

Lucimara Russo ◽

Jean Felix de Oliveira ◽

Clemente Augusto Souza Tanajura ◽

...

Keyword(s):

Data Assimilation ◽

Ocean Model ◽

Sea Surface ◽

Naval Research ◽

Southwest Atlantic ◽

Argo Data ◽

Ocean Data Assimilation ◽

Hybrid Coordinate Ocean Model ◽

Oceanographic Data ◽

The One

ABSTRACT. The objective in this paper is to analyze which Sea Surface Height (SSH) source applied to HYCOM (HYbrid Coordinate Ocean Model) is best suited to numerical prediction of the Southwest Atlantic Ocean. To this end two nested grids were used. One grid for the entire Atlantic Ocean (1/4◦) nesting the grid for the Southwest Atlantic (1/12◦) in the one-way mode. Three forecast experiments with different SSH data sources (Naval Research Laboratory – NRL; Archiving, Validation and Interpolation of Oceanographic Data – AVISO and MERCATOR) applied to constrain the initial conditions and a control forecast experiment without SSH constrain were compared. The comparison of forecasted temperature and salinity profiles with Argo data showed good correlation, over 0.98 for temperature and 0.87 for salinity. The NRL experiment – with SSH obtained by HYCOM+NCODA (Navy Coupled Ocean Data Assimilation System) GLOBAL 1/12◦ analysis was the one that best represented the average temperature and salinity profile with respect to the Argo data. Keywords: HYCOM, numerical modeling, ocean prediction, Argo profiler, Taylor diagram. RESUMO. O objetivo deste trabalho é avaliar qual a fonte de dados de ASM (Altura da Superfície do Mar) imposta no modelo HYCOM (HYbrid Coordinate Ocean Model) é mais adequada para a previsão numérica do Oceano Atlântico Sudoeste. Para isto foram utilizadas duas grades aninhadas, uma grade para todo o Oceano Atlântico (1/4◦) aninhada no modo one-way a outra grade para o Atlântico Sudoeste (1/12◦). Foram realizados três experimentos com diferentes campos de ASM (Naval Research Laboratory – NRL; Archiving, Validation and Interpolation of Oceanographic data – AVISO e MERCATOR) impostos na condição inicial e um experimento controle no qual não foi usada fonte de ASM externa. A comparação dos perfis de temperatura e salinidade entre os dados observados e os resultados do modelo apresentou boa correlação, maior que 0,98 para a temperatura e 0,87 para a salinidade. O experimento NRL com ASM total obtido dos resultados do HYCOM+NCODA (Navy Coupled Ocean Data Assimilation) GLOBAL 1/12◦ foi o que melhor representou o perfil médio de temperatura e salinidade observado. Palavras-chave: HYCOM, modelagem numérica, previsão oceânica, perfiladores Argo, diagrama de Taylor.

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Dense CTD survey versus glider fleet sampling: comparing data assimilation performance in a regional ocean model west of Sardinia

Ocean Science ◽

10.5194/os-14-1069-2018 ◽

2018 ◽

Vol 14 (5) ◽

pp. 1069-1084 ◽

Cited By ~ 6

Author(s):

Jaime Hernandez-Lasheras ◽

Baptiste Mourre

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Forecast Error ◽

Model Error ◽

Western Mediterranean ◽

Ocean Model ◽

Optimal Interpolation ◽

Trial Duration ◽

Sea Trial ◽

Regional Ocean Model

Abstract. The REP14-MED sea trial carried out off the west coast of Sardinia in June 2014 provided a rich set of observations from both ship-based conductivity–temperature–depth (CTD) probes and a fleet of underwater gliders. We present the results of several simulations assimilating data either from CTDs or from different subsets of glider data, including up to eight vehicles, in addition to satellite sea level anomalies, surface temperature and Argo profiles. The Western Mediterranean OPerational forcasting system (WMOP) regional ocean model is used with a local multi-model ensemble optimal interpolation scheme to recursively ingest both lower-resolution large-scale and dense local observations over the whole sea trial duration. Results show the capacity of the system to ingest both types of data, leading to improvements in the representation of all assimilated variables. These improvements persist during the 3-day periods separating two analyses. At the same time, the system presents some limitations in properly representing the smaller-scale structures, which are smoothed out by the model error covariances provided by the ensemble. An evaluation of the forecasts using independent measurements from shipborne CTDs and a towed ScanFish deployed at the end of the sea trial shows that the simulations assimilating initial CTD data reduce the error by 39 % on average with respect to the simulation without data assimilation. In the glider-data-assimilative experiments, the forecast error is reduced as the number of vehicles increases. The simulation assimilating CTDs outperforms the simulations assimilating data from one to four gliders. A fleet of eight gliders provides similar performance to the 10 km spaced CTD initialization survey in these experiments, with an overall 40 % model error reduction capacity with respect to the simulation without data assimilation when comparing against independent campaign observations.

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The role of cross‐domain error correlations in strongly coupled 4D‐Var atmosphere–ocean data assimilation

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.3802 ◽

2020 ◽

Vol 146 (730) ◽

pp. 2450-2465 ◽

Cited By ~ 1

Author(s):

Polly J. Smith ◽

Amos S. Lawless ◽

Nancy K. Nichols

Keyword(s):

Data Assimilation ◽

Strongly Coupled ◽

Ocean Data Assimilation ◽

Cross Domain

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

Ocean Science ◽

10.5194/os-8-333-2012 ◽

2012 ◽

Vol 8 (3) ◽

pp. 333-344 ◽

Cited By ~ 25

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.

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