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 A Hybrid Global Ocean Data Assimilation System at NCEP

2015 ◽  
Vol 143 (11) ◽  
pp. 4660-4677 ◽  
Author(s):  
Stephen G. Penny ◽  
David W. Behringer ◽  
James A. Carton ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Global Ocean ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

scholarly journals Impacts of mean dynamic topography on a regional ocean assimilation system

Ocean Science ◽  
2015 ◽  
Vol 11 (5) ◽  
pp. 829-837 ◽  
Author(s):  
C. Yan ◽  
J. Zhu ◽  
C. A. S. Tanajura
Keyword(s):  
Data Assimilation ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface ◽  
The Mean

Abstract. An ocean data assimilation system was developed for the Pacific–Indian oceans with the aim of assimilating altimetry data, sea surface temperature, and in situ measurements from Argo (Array for Real-time Geostrophic Oceanography), XBT (expendable bathythermographs), CTD (conductivity temperature depth), and TAO (Tropical Atmosphere Ocean). The altimetry data assimilation requires the addition of the mean dynamic topography to the altimetric sea level anomaly to match the model sea surface height. The mean dynamic topography is usually computed from the model long-term mean sea surface height, and is also available from gravimetric satellite data. In this study, the impact of different mean dynamic topographies on the sea level anomaly assimilation is examined. Results show that impacts of the mean dynamic topography cannot be neglected. The mean dynamic topography from the model long-term mean sea surface height without assimilating in situ observations results in worsened subsurface temperature and salinity estimates. Even if all available observations including in situ measurements, sea surface temperature measurements, and altimetry data are assimilated, the estimates are still not improved. This proves the significant impact of the MDT (mean dynamic topography) on the analysis system, as the other types of observations do not compensate for the shortcoming due to the altimetry data assimilation. The gravimeter-based mean dynamic topography results in a good estimate compared with that of the experiment without assimilation. The mean dynamic topography computed from the model long-term mean sea surface height after assimilating in situ observations presents better results.

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
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