Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico

doi:10.1007/s11707-013-0377-8

Front Earth Sci

2013, Vol. 7

Issue (3) : 271-281 https://doi.org/10.1007/s11707-013-0377-8

RESEARCH ARTICLE

Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico

Zhibin SUN¹(

), Lie-Yauw OEY², Yi-Hui ZHOU³

1. Universities Space Research Association, Columbia, MD 21044, USA; 2. Princeton University, AOS, Sayre Hall, Forrestal Campus, Princeton, NJ 08544, USA; 3. Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA

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Abstract

A new data assimilation algorithm (Quasi-EnKF) in ocean modeling, based on the Ensemble Kalman Filter scheme, is proposed in this paper. This algorithm assimilates not only surface measurements (sea surface height), but also deep (~2000 m) temperature observations from the Gulf of Mexico into regional ocean models. With the use of the Princeton Ocean Model (POM), integrated for approximately two years by assimilating both surface and deep observations, this new algorithm was compared to an existing assimilation algorithm (Mellor-Ezer Scheme) at different resolutions. The results show that, by comparing the observations, the new algorithm out-performs the existing one.

Keywords data assimilation deep observation Gulf of Mexico

Corresponding Author(s): SUN Zhibin,Email:sunzhib1@gmail.com

Issue Date: 05 September 2013

Cite this article:

Zhibin SUN,Lie-Yauw OEY,Yi-Hui ZHOU. Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico[J]. Front Earth Sci, 2013, 7(3): 271-281.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0377-8
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I3/271

Fig.1 (a) Model region with isobaths (m) and transports (Sv) specified at the eastern open boundary. This model region has a horizontal resolution of 5-20 km, and 25 sigma-levels. The high-resolution nested region is approximately west of 78°W (west of the blue dashed line), with a 2-5 km horizontal resolution and 25 and 41 sigma-levels. The red-dashed rectangle indicates the northeast central Gulf of Mexico where the MMS’ 2003-2004 observations were taken. (b) The mooring locations and names (see text) superimposed on a color map of modeled SSH (m) averaged between 2003 and 2004. The Ls indicate full-depth moorings consisting of C/T/D, ADCPs, and current-meters. Other higher-alphabetized stations are deep current measurements only: 500 m and 100 m above the bottom. Not shown are 25 PIES stations evenly spaced and covering the main portion (88°-92°W, 25.5°-28°N) of the observational region.

Fig.2 Averaged time-dependent correlation coefficient between and (average region is (-88oW, -86oW) × (24oN, 26oN)).

Fig.3 Grid points in QEnKF scheme. Plus-sign points are observational grids. Solid-grid points are model grids. (a) 2-D grid points in QEnKF SSHA scheme: On a horizontal (,) plane, for each observational grid within a given small region (dashed circle), find four model grids that construct the smallest quadrilateral enclosing the observational grid. (b) 3-D grid points in QEnKF mooring scheme: for each observational grid within a given small 3-D region (ball around the mid-panel), find four model grids that construct the smallest quadrilateral enclosing the observational grid on the same horizontal plane, four model grids with the same (,)’s coordinates at the lower horizontal plane, and another four at the upper horizontal plane. Thus, there are twelve model grids enclosing one observational grid.

Tab.1 Seven experiments using different schemes

Tab.2 Complex correlation coefficient comparisons (,) of the model and observations at L1 mooring location

Tab.3 Complex correlation coefficient comparisons (,) of the model and observations at L2 mooring location

Tab.4 Complex correlation coefficient comparisons (,) of the model and observations at L3 mooring location

Tab.5 Complex correlation coefficient comparisons (,) of the model and observations at L4 mooring locations

Tab.6 Mean spatial correlation within the region north of 23°N, west of 84°W, and in water with depths>500 m.

Tab.7 Statistics of comparison of temperature time series between model and mooring observations.

Tab.8 Temperature temporal correlation coefficient at different depths for each mooring between model and observations.

Fig.4 SSHA temporal correlation coefficient for case D within the region where water depth is greater than 500 m; -axis is longitude, -axis is latitude.

Fig.5 Temperature comparisons at different depths of the L4-mooring location between model (solid curve) and mooring observations (dashed curve) for case C; -axis is date, -axis is temperature, CC is the correlation coefficient.

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