COSTA a problem solving environment for data assimilation applied for hydrodynamical modelling

Velzen, Nils van; Verlaan, Martin

doi:10.1127/0941-2948/2007/0241

Cited by 18 publications

(18 citation statements)

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“…Many of these applications rely on assimilating traditional in-situ observations such as discharge, soil moisture and snowpack measurements into hydrologic models to improve predictions of streamflow and other hydrologic variables (e.g., Seo et al, 2003Seo et al, , 2009Vrugt et al, 2005;Weerts and El Serafy, 2006;Clark et al, 2008a;Komma et al, 2008;Moradkhani and Sorooshian, 2008;Thirel et al, 2010a, b). In recent years, increasing availability of satellite observations (e.g., van Dijk and Renzullo, 2011) has generated unprecedented research activity into assimilating these remotely sensed retrievals of various quantities, such as soil moisture (e.g., Pauwels et al, 2001;De Lannoy et al, 2007;Moradkhani and Sorooshian, 2008;Reichle et al, 2008;Yirdaw et al, 2008;Kumar et al, 2009;Brocca et al, 2010;Montzka et al, 2011;Peters-Lidard et al, 2011;Liu et al, 2012), snow water equivalent and/or snow cover area or extent (e.g., Rodell and Houser, 2004;Lee et al, 2005;Andreadis and Lettenmaier, 2006;Liston and Hiemstra, 2008;Zaitchik et al, 2008;Durand et al, 2009;Kolberg and Gottschalk, 2010;Kuchment et al, 2010;DeChant and Moradkhani, 2011a;De Lannoy et al, 2012), surface water elevation (e.g., Alsdorf et al, 2007;Montanari et al, 2009;Neal et al, 2009;Giustarini et al, 2011), terrestrial water storage …”

Section: Introductionmentioning

confidence: 99%

“…In recent years, increasing availability of satellite observations (e.g., van Dijk and Renzullo, 2011) has generated unprecedented research activity into assimilating these remotely sensed retrievals of various quantities, such as soil moisture (e.g., Pauwels et al, 2001;De Lannoy et al, 2007;Moradkhani and Sorooshian, 2008;Reichle et al, 2008;Yirdaw et al, 2008;Kumar et al, 2009;Brocca et al, 2010;Montzka et al, 2011;Peters-Lidard et al, 2011;Liu et al, 2012), snow water equivalent and/or snow cover area or extent (e.g., Rodell and Houser, 2004;Lee et al, 2005;Andreadis and Lettenmaier, 2006;Liston and Hiemstra, 2008;Zaitchik et al, 2008;Durand et al, 2009;Kolberg and Gottschalk, 2010;Kuchment et al, 2010;DeChant and Moradkhani, 2011a;De Lannoy et al, 2012), surface water elevation (e.g., Alsdorf et al, 2007;Montanari et al, 2009;Neal et al, 2009;Giustarini et al, 2011), terrestrial water storage (Zaitchik et al, 2008) and land surface temperature , among others. These DA applications were developed for a variety of models ranging from physically-based land-surface models (e.g., Albergel et al, 2008;Nagarajan et al, 2010) to distributed hydrologic models (e.g., Clark et al, 2008a;Rakovec et al, 2012a, b) and conceptual rainfall-runoff models (e.g., Aubert et al, 2003;Seo et al, 2003Seo et al, , 2009Morad...…”

Section: Introductionmentioning

confidence: 99%

“…These include, for example, the one-, two-, three-and fourdimensional variational algorithms (1D-, 2D-, 3D-, and 4D-VAR, e.g., Seo et al, 2003Seo et al, , 2009Valstar et al, 2004), extended or ensemble Kalman filtering (EKF or EnKF, e.g., Moradkhani et al, 2005b;Slater and Clark, 2006;Weerts and El Serafy, 2006;Shamir et al, 2010), particle filtering (e.g., Moradkhani et al, 2005a;Weerts and El Serafy, 2006;Matgen et al, 2010;DeChant and Moradkhani, 2012), H-infinity filters (Wang and Cai, 2008), hybrid EnKF or 4D-VAR approaches (e.g., Zhang et al, 2009), and other Bayesian approaches (e.g., Reggiani and Weerts, 2008;Todini, 2008;Reggiani et al, 2009). While most DA applications have focused on updating hydrologic model states (e.g., soil moisture and snow water equivalent), recent research has also examined the benefits of estimating model states and model parameters simultaneously (e.g., Moradkhani et al, 2005a, b Vrugt et al, 2005;Lu et al, 2010;Leisenring and Moradkhani, 2011;Nie et al, 2011) as well as the possibility of model structure identification and uncertainty estimation (e.g., Neuman, 2003;Bulygina and Gupta, 2010;Hsu et al, 2009;Parrish et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

375

296

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Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters.The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, asPublished by Copernicus Publications on behalf of the European Geosciences Union. Y. Liu et al.: Advancing data assimilation in operational hydrologic forecastingwell as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

375

296

View full text Add to dashboard Cite

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“…It has been shown that the performance is close to dedicated implementations (van Velzen and Verlaan 2007) and furthermore can automatically propagate the ensemble of models either serially or concurrently. …”

Section: Openda Data Assimilation Toolboxmentioning

confidence: 99%

OpenDA-NEMO framework for ocean data assimilation

2016

Self Cite

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Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations.

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“…Finally, the filter library uses the information to update and estimate the state of the model. The interfacing of the modules has been standardized together with the Delft University data assimilation research group that initiated the COSTA development (Van Velzen 2006). Similarly, the set of functions and tasks that needs to be present within in the modules has been defined jointly.…”

Section: Structure Of the Modelling Environmentmentioning

confidence: 99%

Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment

et al. 2007

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The first part of the paper describes a portable and flexible data assimilation environment (DATools) for easy application of data assimilation and calibration techniques to models that are used in smaller-scale engineering applications, for example to guide temporary offshore construction works, marine surveying and salvage operations. These applications are characterized by the need for detailed forecasts of often strongly non-linear marine behaviour in shallow-shelf seas under constraints of limited field measurements and absence of large-scale computing facilities. The applications are often prepared, and their results are interpreted by end-user engineers in field offices, not well acquainted with the data assimilation theory. The DATools data assimilation environment has been designed to facilitate such applications. It presently features an ensemble Kalman filter and two particle filters. These can be coupled to any process model in a standardized way through the so-called Published Interface that is used in an increasing number of flood-forecasting applications across Europe. The design of the system and its main modules are discussed, looking at the system from a non-specialist user perspective and focusing on modularity, transparency, user guidance, intuitive and flexible uncertainty prescription. The second part of the paper describes a typical example application of data assimilation in an engineering environment for which the modelling environment has been developed: the daily forecast of current and salinity profiles to guide construction works in Osaka Bay. An ensemble Kalman filter-based steady-state Kalman filter is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly nonlinear stratified shallow bay. Calibration results are summarised for both hindcast mode and forecast mode. After assessment of the results, a second, improved model setup is described, including now assimilation of temperature and water level as well as currents and salinity. The calibration results are shown to improve further. Both applications show that significant forecast improvements can be possible with data assimilation in typical engineering applications, notwithstanding operational constraints such as limited measurement data and restricted computational infrastructure. It is argued that the described portable data assimilation environment is well suited for temporary engineering applications as it provides typical end-user functionalities for data assimilation and will so allow the user to focus on the characteristics of the physical processes and interpretation of the results instead of on details of the mathematical techniques. This should make practical data assimilation accessible for a much wider (end) user community in oceanography and offshore.

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COSTA a problem solving environment for data assimilation applied for hydrodynamical modelling

Cited by 18 publications

References 0 publications

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

OpenDA-NEMO framework for ocean data assimilation

Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment

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