Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters

Hoteit, Ibrahim; Triantafyllou, G.; Petihakis, George

doi:10.5194/angeo-23-3171-2005

Cited by 28 publications

(19 citation statements)

References 35 publications

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“…Ishizaka, 1990;Anderson et al, 2000;Carmillet et al, 2001;Popava et al, 2002;Beşiktepe et al, 2003;Triantafyllou et al, 2003;Hoteit et al, 2005;Fontana et al, 2010). Natvik and Evensen (2003) assimilated remotely sensed chlorophyll data into a model of the North Atlantic.…”

Section: A Ford Et Al: Assimilating Globcolour Ocean Colour Datamentioning

confidence: 99%

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

et al. 2012

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Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physicalbiogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO 2 , but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

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Section: A Ford Et Al: Assimilating Globcolour Ocean Colour Datamentioning

confidence: 99%

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

et al. 2012

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“…Nevertheless, the use of data assimilation complementing ecosystem modeling efforts has gained widespread attention (e.g., Hoteit et al, 2003Hoteit et al, , 2005Allen et al, 2003;Natvik and Evensen, 2003;Triantafyllou et al, 2007Triantafyllou et al, , 2013While et al, 2012;Teruzzi et al, 2014). Data assimilation into ecosystem models has focused both on parameter optimization and on state and flux estimations (Gregg et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis

2017

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Abstract. Long-term oxygen and nutrient transports in the Baltic Sea are reconstructed using the Swedish Coastal and Ocean Biogeochemical model (SCOBI) coupled to the Rossby Centre Ocean model (RCO). Two simulations with and without data assimilation covering the period 1970-1999 are carried out. Here, the "weakly coupled" scheme with the Ensemble Optimal Interpolation (EnOI) method is adopted to assimilate observed profiles in the reanalysis system. The reanalysis shows considerable improvement in the simulation of both oxygen and nutrient concentrations relative to the free run. Further, the results suggest that the assimilation of biogeochemical observations has a significant effect on the simulation of the oxygen-dependent dynamics of biogeochemical cycles. From the reanalysis, nutrient transports between sub-basins, between the coastal zone and the open sea, and across latitudinal and longitudinal cross sections are calculated. Further, the spatial distributions of regions with nutrient import or export are examined. Our results emphasize the important role of the Baltic proper for the entire Baltic Sea, with large net transport (export minus import) of nutrients from the Baltic proper into the surrounding sub-basins (except the net phosphorus import from the Gulf of Riga and the net nitrogen import from the Gulf of Riga and Danish Straits). In agreement with previous studies, we found that the Bothnian Sea imports large amounts of phosphorus from the Baltic proper that are retained in this sub-basin. For the calculation of sub-basin budgets, the location of the lateral borders of the sub-basins is crucial, because net transports may change sign with the location of the border. Although the overall transport patterns resemble the results of previous studies, our calculated estimates differ in detail considerably.

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“…Ensemble DA schemes and square root filters have been utilized in idealized 1D studies [e.g., Eknes and Evensen, 2002;Raick et al, 2007]. More realistic data-assimilative ecosystem simulations with simplified Kalman filters [Hoteit et al, 2004], quasi-local Kalman filters [Hoteit et al, 2005] and singular evolutive interpolated Kalman filters [Triantafyllou et al, 2003] have been successful in the Mediterranean. In semienclosed coastal bays such as Massachusetts Bay, real ocean data and coupled (sub)mesoscales physical-biogeochemical models have been combined using optimal interpolation [Beşiktepe et al, 2003] and ensemble prediction schemes [Lermusiaux, 2006;Lermusiaux et al, 2006a].…”

Section: Overview Of Recent Biogeochemical-ecosystem Ocean Data Assimmentioning

confidence: 99%

Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation

Cossarini¹,

Lermusiaux²,

Solidoro³

2009

J. Geophys. Res.

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[1] An ensemble data assimilation scheme, Error Subspace Statistical Estimation (ESSE), is utilized to investigate the seasonal ecosystem dynamics of the Lagoon of Venice and provide guidance on the monitoring and management of the Lagoon, combining a rich data set with a physical-biogeochemical numerical estuary-coastal model. Novel stochastic ecosystem modeling components are developed to represent prior uncertainties in the Lagoon dynamics model, measurement model, and boundary forcing by rivers, open-sea inlets, and industrial discharges. The formulation and parameters of these additive and multiplicative stochastic error models are optimized based on data-model forecast misfits. The sensitivity to initial and boundary conditions is quantified and analyzed. Half-decay characteristic times are estimated for key ecosystem variables, and their spatial and temporal variability are studied. General results of our uncertainty analyses are that boundary forcing and internal mixing have a significant control on the Lagoon dynamics and that data assimilation is needed to reduce prior uncertainties. The error models are used in the ESSE scheme for ensemble uncertainty predictions and data assimilation, and an optimal ensemble dimension is estimated. Overall, higher prior uncertainties are predicted in the central and northern regions of the Lagoon. On the basis of the dominant singular vectors of the ESSE ensemble, the two major northern rivers are the biggest sources of dissolved inorganic nitrogen (DIN) uncertainty in the Lagoon. Other boundary sources such as the southern rivers and industrial discharges can dominate uncertainty modes on certain months. For dissolved inorganic phosphorus (DIP) and phytoplankton, dominant modes are also linked to external boundaries, but internal dynamics effects are more significant than those for DIN. Our posterior estimates of the seasonal biogeochemical fields and of their uncertainties in 2001 cover the whole Lagoon. They provide the means to describe the ecosystem and guide local environmental policies. Specifically, our findings and results based on these fields include the temporal and spatial variability of nutrient and plankton gradients in the Lagoon; dynamical connections among ecosystem fields and their variability; strengths, gradients and mechanisms of the plankton blooms in late spring, summer, and fall; reductions of uncertainties by data assimilation and thus a quantification of data impacts and data needs; and, finally, an assessment of the water quality in the Lagoon in light of the local environmental legislation.

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Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters

Cited by 28 publications

References 35 publications

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis

Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation

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