Impact of along-track altimeter sea surface height anomaly assimilation on surface and sub-surface currents in the Bay of Bengal

Along-track sea surface height anomaly (SSHA) data from the Jason-1 and Jason-2 satellites were assimilated into the ocean model HYCOM from July 1, 2009 until December 31, 2009. A new and simple approach to overcome the bias between the model and observed SSHA was proposed. It focuses on the meso-scale differences between the data and the model along each satellite track. An optimal interpolation method and the Cooper and Haines (1996) scheme (C&H) were employed to produce a SSHA analysis field and to adjust model layer thicknesses over the Atlantic METAREA V. The corrected model state was used as initial condition for the next assimilation cycle. SSHA data with a 7-day window were assimilated in 3-day intervals centered in the SSHA data window and the C&H scheme was applied taking the SSHA analysis. A control run without assimilation was also performed. The results showed that the model SSHA was completely reorganized by the end of the experiment. The modifications of SSHA were compared to the American Navy HYCOM+NCODA system and AVISO data. Maximum error was reduced from 0.7 m to 0.2 m by assimilation. Comparisons were also made with the Argo temperature and salinity vertical profiles. Improvements in the currents and volume transport were also produced by assimilation. The impact in temperature was in general positive, but there was no substantial modification in salinity.

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Sea surface height anomaly and upper ocean temperature over the Indian Ocean during contrasting monsoons

Dynamics of Atmospheres and Oceans ◽

10.1016/j.dynatmoce.2016.04.002 ◽

2016 ◽

Vol 75 ◽

pp. 1-21 ◽

Cited By ~ 8

Author(s):

Anitha Gera ◽

A.K. Mitra ◽

D.K. Mahapatra ◽

I.M. Momin ◽

E.N. Rajagopal ◽

...

Keyword(s):

Indian Ocean ◽

Sea Surface Height ◽

Sea Surface ◽

Height Anomaly ◽

Upper Ocean ◽

Ocean Temperature ◽

The Indian Ocean ◽

Sea Surface Height Anomaly

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Sea surface height anomaly and geostrophic velocity from altimetry measurements over the Arctic Ocean (2011–2018)

10.5194/essd-2021-170 ◽

2021 ◽

Author(s):

Francesca Doglioni ◽

Robert Ricker ◽

Benjamin Rabe ◽

Torsten Kanzow

Keyword(s):

Large Scale ◽

Sea Surface Height ◽

Sea Surface ◽

The Arctic ◽

Height Anomaly ◽

Near Surface ◽

Geostrophic Velocity ◽

Surface Ocean ◽

Surface Circulation ◽

Sea Surface Height Anomaly

Abstract. In recent decades the decline of the Arctic sea ice has modified vertical momentum fluxes from the atmosphere to the ice and the ocean, thereby affecting the surface circulation. In the past ten years satellite altimetry has contributed to understand these changes. However, data from ice-covered regions require dedicated processing, originating inconsistency between ice-covered and open ocean regions in terms of biases, corrections and data coverage. Thus, efforts to generate consistent Arctic-wide datasets are still required to enable the study of the Arctic Ocean surface circulation at basin-wide scales. Here we provide and assess a monthly gridded dataset of sea surface height anomaly and geostrophic velocity. This dataset is based on Cryosat-2 observations over ice-covered and open ocean areas of the Arctic up to 88° N for the period 2011 to 2018, interpolated using the Data-Interpolating Variational Analysis (DIVA) method. Geostrophic velocity was not available north of 82° N before this study. To examine the robustness of our results, we compare the generated fields to one independent altimetry dataset and independent data of ocean bottom pressure, steric height and near-surface ocean velocity from moorings. Results from the comparison to near-surface ocean velocity show that our geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km. We further discuss the seasonal cycle of sea surface height and geostrophic velocity in the context of previous literature. Large scale features emerge, i.e. Arctic-wide maximum sea surface height between October and January, with the highest amplitude over the shelves, and basin wide seasonal acceleration of Arctic slope currents in winter. We suggest that this dataset can be used to study not only the large scale sea surface height and circulation but also the regionally confined boundary currents. The dataset is available in netCDF format from PANGAEA at [data currently under review].

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The Connection between the Sea Surface Height Anomaly Preceding the Indian Ocean Dipole and Summer Rainfall in China

Atmospheric and Oceanic Science Letters ◽

10.1080/16742834.2015.11447266 ◽

2015 ◽

Vol 8 (4) ◽

pp. 238-243

Author(s):

Duan Xin-Yu ◽

Liu Na ◽

Li Shuanglin

Keyword(s):

Indian Ocean ◽

Indian Ocean Dipole ◽

Summer Rainfall ◽

Sea Surface Height ◽

Sea Surface ◽

Height Anomaly ◽

The Indian Ocean ◽

Sea Surface Height Anomaly

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Statistical Prediction of the South China Sea Surface Height Anomaly

Advances in Meteorology ◽

10.1155/2015/907313 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Caixia Shao ◽

Weimin Zhang ◽

Chunjian Sun ◽

Xinmin Chai ◽

Zhimin Wang

Keyword(s):

Time Series ◽

South China Sea ◽

South China ◽

Decadal Variability ◽

Sea Surface Height ◽

Sea Surface ◽

Statistical Prediction ◽

Height Anomaly ◽

China Sea ◽

Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.

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