A problem-solving environment for data assimilation in air quality modelling

Velzen, Nils van; Segers, Arjo

doi:10.1016/j.envsoft.2009.08.008

Cited by 22 publications

(9 citation statements)

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“…OpenDA is a generic framework for parameter calibration and data assimilation applications (El Serafy et al 2007;van Velzen and Segers 2010). It makes use of the fact that it is possible, and convenient, to separate the code of data assimilation methods from that of the forecast models.…”

Section: Openda Data Assimilation Toolboxmentioning

confidence: 99%

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-meansquare error (RMSE) ∼7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

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Section: Openda Data Assimilation Toolboxmentioning

confidence: 99%

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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“…Different state updating techniques available in www.openda.org are outlines in [11]. Within this research, we apply a relative simple Ensemble Kalman Filter, for which we put minimum uncertainty on the observed states and maximum uncertainty on the simulated data.…”

Section: ) State Updatingmentioning

confidence: 99%

Data assimilation for supporting optimum control in large-scale river networks

Schwanenberg

Breukelen

Hummel

2011

2011 International Conference on Networking, Sensing and Control

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We present a Nonlinear Model Predictive Control (NMPC) algorithm for real-time control of large-scale river networks in delta areas. The algorithm consists of an iterative, finite-horizon optimization of the system over a short-term control horizon. The underlying set of nonlinear internal process models represents relevant physical phenomena such as flow routing in the river network, and the dynamics of hydraulic structures.Data assimilation (DA) techniques turn out to be a key factor for the practical implementation of such schemes and may serve various purposes. First of all, DA contributes to the offline system identification of reduced internal models by parameter optimization. Secondly, we apply DA in an operational mode for model updating by adapting parameters, states, or outputs of the internal model for improving its lead time accuracy.The framework of DA and NMPC is applied on the control of a complex river network in the Dutch delta of Rhine River. We discuss the performance of a derivative-free optimization algorithm for calibrating the roughness coefficients of the underlying kinematic wave model and online parameter updating. Furthermore, we present the application of an Ensemble Kalman Filter (EKF) for updating model states as well as an output correction based on an AR(1) model. The contribution of these techniques in relation to the MPC performance is discussed in detail. Sx perf S perf J J P J J 978-1-4244-9573-3/11/$26.00 ©2011 IEEE

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“…The data assimilation was conducted using the open source data assimilation toolbox OpenDA; a generic framework for parameter calibration and data assimilation applications [El Serafy et al, 2007, Van Velzen andSegers, 2010]. The improvement of the tidal water level modeling obtained with a Kalman filter is to a large extent dependent on the observational network on which one relies.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%