Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI)

Ridler, Marc; Velzen, Nils van; Hummel, Stef; Sandholt, Inge; Falk, Anne Katrine Vinther; Heemink, A.W.; Madsen, Henrik

doi:10.1016/j.envsoft.2014.02.008

Cited by 39 publications

(23 citation statements)

References 39 publications

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“…For instance, EU FP5 projects like EFFS (Pappenberger et al, 2005) showed the applicability and usefulness of the ensemble approach for hydrological forecasting. Since then, many EU and other collaborative research projects focusing on this topic, such as HEPEX (Schaake et al, 2007) and MAP-D-Phase (Rotach et al, 2009) have been, and are being, conducted. Concurrently, the research efforts of Davolio et al (2013), Vincendon et al (2011) and Rebora et al (2006) demonstrated the improvement in flood forecasting brought about through the use of convection-resolving horizontal resolutions in the meteorological model rainfall sources and also illustrated the advantages of perturbing model parameters and rainfall sources, and in employing the dynamical downscaling of a large-scale ensemble prediction system (EPS).…”

Section: A Hally Et Al: Hydrometeorological Multi-model Ensemble Simentioning

confidence: 99%

Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project

Hally

Caumont

Garrote

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (Rain-FARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus giving a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios ( > 1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash flood early-warning perspective.

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Section: A Hally Et Al: Hydrometeorological Multi-model Ensemble Simentioning

confidence: 99%

Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project

Hally

Caumont

Garrote

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…The MIKE SHE model is coupled with a generic DA library that handles the time propagation and update of the model ensemble based on the ETKF (Ridler et al, 2014b).…”

Section: Ensemble Transform Kalman Filtermentioning

confidence: 99%

“…Lee et al (2011) used a variational assimilation approach to assimilate streamflow and in situ soil moisture, to correct the soil moisture profiles within the HL-RDHM model. Ridler et al (2014b) developed a generic DA framework that enables coupling of hydrological models with the OpenDA library (http://www.openda.org) using the OpenMI (Open Model Interface; Gregersen et al, 2007), and applied it with the MIKE SHE model. Han et al (2015) developed an open-source multivariate DA framework (DasPy) for the Community Land Model.…”

Section: Introductionmentioning

confidence: 99%

Multivariate hydrological data assimilation of soil moisture and groundwater head

Zhang

Madsen

Ridler

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.

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“…OpenDA (2014) provides a number of algorithms for model calibration and assimilation and is suitable to be connected to any kind of environmental model (e.g. Ridler et al, 2014).…”

Section: Data and Hydrological Modelmentioning

confidence: 99%

Operational aspects of asynchronous filtering for flood forecasting

Rakovec

Weerts

Sumihar

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.

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Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI)

Cited by 39 publications

References 39 publications

Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project

Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project

Multivariate hydrological data assimilation of soil moisture and groundwater head

Operational aspects of asynchronous filtering for flood forecasting

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