scholarly journals Development of a variational data assimilation system for the diurnal cycle of sea surface temperature

2013 ◽  
Vol 118 (6) ◽  
pp. 2845-2862 ◽  
Author(s):  
J. While ◽  
M. Martin
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Diurnal Cycle ◽  
Variational Data Assimilation ◽  
Sea Surface ◽  
Data Assimilation System ◽  
Assimilation System

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 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 Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model

2015 ◽  
Vol 2015 ◽  
pp. 1-17
Author(s):  
Xuefeng Zhang ◽  
Dong Li ◽  
Peter C. Chu ◽  
Lianxin Zhang ◽  
Wei Li
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
State Estimation ◽  
The State ◽  
Variational Data Assimilation ◽  
Sea Surface ◽  
Superior Performance ◽  
Assimilation Experiment ◽  
Gradient Diffusion

Sequential, adaptive, and gradient diffusion filters are implemented into spatial multiscale three-dimensional variational data assimilation (3DVAR) as alternative schemes to model background error covariance matrix for the commonly used correction scale method, recursive filter method, and sequential 3DVAR. The gradient diffusion filter (GDF) is verified by a two-dimensional sea surface temperature (SST) assimilation experiment. Compared to the existing DF, the new GDF scheme shows a superior performance in the assimilation experiment due to its success in extracting the spatial multiscale information. The GDF can retrieve successfully the longwave information over the whole analysis domain and the shortwave information over data-dense regions. After that, a perfect twin data assimilation experiment framework is designed to study the effect of the GDF on the state estimation based on an intermediate coupled model. In this framework, the assimilation model is subject to “biased” initial fields from the “truth” model. While the GDF reduces the model bias in general, it can enhance the accuracy of the state estimation in the region that the observations are removed, especially in the South Ocean. In addition, the higher forecast skill can be obtained through the better initial state fields produced by the GDF.

scholarly journals Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

2013 ◽  
Vol 5 (6) ◽  
pp. 3123-3139 ◽  
Author(s):  
Yasumasa Miyazawa ◽  
Hiroshi Murakami ◽  
Toru Miyama ◽  
Sergey Varlamov ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ensemble Kalman Filter ◽  
Sea Surface
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