Evaluating the Impact of the Number of Satellite Altimeters Used in an Assimilative Ocean Prediction System

2010 ◽  
Vol 27 (3) ◽  
pp. 528-546 ◽  
Author(s):  
Robert W. Helber ◽  
Jay F. Shriver ◽  
Charlie N. Barron ◽  
Ole Martin Smedstad
Keyword(s):  
Data Assimilation ◽  
Statistical Error ◽  
Global Ocean ◽  
Height Anomaly ◽  
Naval Research ◽  
Thermocline Depth ◽  
Dynamic Height ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001–03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1° radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods.

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.

Development of a Mesoscale Ensemble Data Assimilation System at the Naval Research Laboratory

2013 ◽  
Vol 28 (6) ◽  
pp. 1322-1336 ◽  
Author(s):  
Qingyun Zhao ◽  
Fuqing Zhang ◽  
Teddy Holt ◽  
Craig H. Bishop ◽  
Qin Xu
Keyword(s):  
Data Assimilation ◽  
Research Laboratory ◽  
Localization Length ◽  
Naval Research Laboratory ◽  
Naval Research ◽  
Ensemble Size ◽  
The Impact ◽  
Data Assimilation System ◽  
Scale Data ◽  
Assimilation System

Abstract An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.

Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System

1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
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 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.

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