A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS

Wang, Keguang; Debernard, Jens Boldingh; Sperrevik, Ann Kristin; Isachsen, Pål Erik; Lee, Thomas

doi:10.3189/2013aog62a138

Cited by 27 publications

(43 citation statements)

References 26 publications

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“…A number of studies have leveraged the extensive coverage of sea ice concentration (both spatially and temporally) to examine the assimilation of satellite-based ice fraction in stand-alone sea ice models [45][46][47][48], sea ice-ocean coupled models [49][50][51][52][53][54][55], and fully coupled climate models [56][57][58]. These studies demonstrated that assimilating sea ice fraction successfully reduces the models' bias and significantly improves the predictive skill of regional distribution of the ice cover and the total ice extent.…”

Section: Sea Ice Concentrationmentioning

confidence: 99%

Towards reliable Arctic sea ice prediction using multivariate data assimilation

Liu

Chen

et al. 2019

Science Bulletin

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Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ''best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.

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Section: Sea Ice Concentrationmentioning

confidence: 99%

Towards reliable Arctic sea ice prediction using multivariate data assimilation

Liu

Chen

et al. 2019

Science Bulletin

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“…Ice concentration (IC) remains a key parameter in the ice DA due to its importance in the ice model dynamics and the abundance of the respective data from satellites. IC assimilation techniques are usually similar to the ones developed for other ocean state variables and range from nudging (Lindsay & Zhang, 2006;Tietsche et al, 2013) and optimal interpolation (Stark et al, 2008;Wang et al, 2013) to ensemble Kalman filtering (Lisäter et al, 2003;Shlyaeva et al, 2016;Yang et al, 2015) and variational methods (Fenty & Heimbach, 2013;Koldunov et al, 2013Koldunov et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea

et al. 2019

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Numerical experiments with a regional configuration of the CICE model (Los‐Alamos Community sea Ice CodE) in the Beaufort Sea assimilating Special Sensor Microwave Imager/Sounder ice concentration (IC) and CryoSat ice thickness (IT) data acquired for September–December of 2015 are presented. We explore sensitivity of the 24‐hr IT/IC forecast skill to the system updates, which include introduction of the IT assimilation capability, flow‐dependent correlations, and Gaussianization of IC innovations. Experiments with IC data assimilation have shown that the flow‐dependent correlations provide 5–7% improvement of the forecast skill during the freezing period (10 September to 10 November) while Gaussianization contributes an additional improvement of 3–4% in most of the cases. In winter (11 November to 31 December) IC assimilation did not produce any statistically significant improvement of the skill due to the loss of dynamical information in the IC fields associated saturation of the ice cover. In contrast, IT assimilation provides larger improvement in December compared to October–November due to the better coverage of the Beaufort Sea by observations and their higher relative accuracy in winter. Comparison of the IT forecast fields with independent in situ observations by two upward looking sonars demonstrates statistically insignificant improvements. Much better improvement (15–25%) is observed when comparing monthly mean IT assimilation runs against independent Advanced Microwave Scanning Radiometer for Earth observing system (AMSR‐E) data. Introduction of the heuristic in situ IC/IT correlations into the background covariance model did not produce any improvements of the forecast skill.

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“…Different assimilation methods have been applied in coupled ice-ocean models, assimilating ice concentration [6][7][8] , and drift [9] . However, when data assimilation is used in an operational application, the efficiency of computation should be a priority.…”

Section: Introduction*mentioning

confidence: 99%

“…In this paper, we introduce the assimilation of ice concentration into an operational ice forecast system, based on a combined optimal interpolation and nudging method. The method was first introduced into the operational ice forecast system at the Norway Meteorological Institute by Wang et al [8] , and preliminary applications have shown positive results in terms of efficiency and accuracy.…”

Section: Introduction*mentioning

confidence: 99%

Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

Li¹,

Zhang²,

Sun³

et al. 2013

APS

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A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a combined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.

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A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS

Cited by 27 publications

References 26 publications

Towards reliable Arctic sea ice prediction using multivariate data assimilation

Towards reliable Arctic sea ice prediction using multivariate data assimilation

Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea

Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

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