Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

Lyu, Guokun; Wang, Hui; Zhu, Jiang; Wang, Dakui; Xie, Jiping; Liu, Guimei

doi:10.1007/s13131-014-0469-7

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…with an ensemble selected at every assimilation cycle from monthly climatology fields with a three--month moving window around the assimilation time. The same scheme was latter adopted by Xie et al (2011) and Lyu et al (2014) for assimilating SLA data in the South China Sea. In this study, we implemented a similar EnOI scheme but selecting the ensemble on a monthly basis from a climatological dataset of the Red Sea circulation that is assumed to describe the variability of the system.…”

Section: Ensemble Kalman Filteringmentioning

confidence: 99%

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

et al. 2017

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We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e. an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step, and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every three days. Real time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

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Section: Ensemble Kalman Filteringmentioning

confidence: 99%

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

et al. 2017

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“…This section examines how existing hydrographic observations could help reveal Arctic freshwater content changes and identify observational gaps in time and space. Based on the spatiotemporal distribution of profiles compiled by Behrendt et al (2018) and an ensemble optimal interpolation (EnOI) scheme (Evensen, 2003;Lyu et al, 2014), we test to what extent the generated synthetic profiles could help to reconstruct the "true" state (here the AT-LARC08km simulation) during the period 1992 to 2012. Details of the EnOI scheme are given in Appendix A.…”

Section: In Situ Profilersmentioning

confidence: 99%

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

et al. 2022

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Abstract. Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

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“…This section examines to what extent existing hydrographic observations could help reveal Arctic freshwater content changes and identify observational gaps. Based on the spatiotemporal distribution of profiles in the study of Behrendt et al (2018) and an ensemble optimal interpolation (EnOI) scheme (Evensen, 2003;Lyu et al, 2014), we test to what extent existing profiles could help to reconstruct the "true" state (here the ATLARC08km simulation) during the periods 1992 to 2012. Details of the EnOI scheme are given in Appendix A.…”

Section: In-situ Profilersmentioning

confidence: 99%

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Lyu

Serra

Zhou

et al. 2021

Preprint

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Abstract. Two high-resolution model simulations are used to investigate the spatio-temporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods < 30 days, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume ex-changes with the Pacific and Atlantic Oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation (EmPmR), ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. Satellite altimetric observations and Gravity Re-covery and Climate Experiment (GRACE) measurements could be used to infer freshwater content changes in the Canadian Basin at periods longer than one year. The increasing number of profiles seems to capture fresh-water content changes since 2007, encouraging further data synthesis work with a more complicated interpola-tion method. Further, in-situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

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Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

Cited by 11 publications

References 21 publications

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

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