scholarly journals The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system

2014 ◽  
Vol 119 (10) ◽  
pp. 6974-6987 ◽  
Author(s):  
G. Vernieres ◽  
R. Kovach ◽  
C. Keppenne ◽  
S. Akella ◽  
L. Brucker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Salinity Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Improvements to a global ocean data assimilation system through the incorporation of Aquarius surface salinity data

2015 ◽  
Vol 141 (692) ◽  
pp. 2750-2759 ◽  
Author(s):  
Takahiro Toyoda ◽  
Yosuke Fujii ◽  
Tsurane Kuragano ◽  
John P. Matthews ◽  
Hiroto Abe ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Global Ocean ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Salinity Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Impact of Multiple Altimeter Data and Mean Dynamic Topography in a Global Analysis and Forecasting System

2019 ◽  
Vol 36 (7) ◽  
pp. 1255-1266 ◽  
Author(s):  
Mathieu Hamon ◽  
Eric Greiner ◽  
Pierre-Yves Le Traon ◽  
Elisabeth Remy
Keyword(s):  
Data Assimilation ◽  
Sea Surface ◽  
Global Ocean ◽  
Altimeter Data ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Ocean Data Assimilation ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

AbstractSatellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter.

scholarly journals Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

2015 ◽  
Vol 143 (11) ◽  
pp. 4678-4694 ◽  
Author(s):  
D. J. Lea ◽  
I. Mirouze ◽  
M. J. Martin ◽  
R. R. King ◽  
A. Hines ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Coupled Model ◽  
Background Information ◽  
Ocean Data Assimilation ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a “weakly” coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean–sea ice and atmosphere–land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5–10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean–atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

scholarly journals Impacts of OSTIA Sea Surface Temperature in Regional Ocean Data Assimilation System

The Sea ◽  
2015 ◽  
Vol 20 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Ji Hye Kim ◽  
Hyun-Min Eom ◽  
Jong-Kuk Choi ◽  
Sang-Min Lee ◽  
Young-Ho Kim ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Impact of Initialization Methods on the Predictive skill in NorCPM - An Arctic-Atlantic Case Study

2021 ◽  
Author(s):  
Leilane Passos ◽  
Helene Langehaug ◽  
Marius Årthun ◽  
Tor Eldevik ◽  
Ingo Bethke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Complex Data ◽  
Surface Salinity ◽  
Norwegian Sea ◽  
Predictive Skill ◽  
The Impact

Abstract The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

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

Ocean Science ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 257-274 ◽  
Author(s):  
V. Turpin ◽  
E. Remy ◽  
P. Y. Le Traon
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface ◽  
Global Ocean ◽  
Argo Data ◽  
In Situ Observations ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Observing system experiments (OSEs) are carried out over a 1-year period to quantify the impact of Argo observations on the Mercator Ocean 0.25° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system) 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 the Argo data 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 (root mean square) 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 differences is also significant from the surface down to a depth of 2000 m. Differences between in situ 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, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration. The impact of the Argo floats' data assimilation on other model variables is briefly assessed: the improvement of the fit to Argo profiles do not lead globally to unphysical corrections on the sea surface temperature and sea surface height. The main conclusion is that the performance of the Mercator Ocean 0.25° global data assimilation system is heavily dependent on the availability of Argo data.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

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