12 research outputs found
The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas
The HIROMB-BOOS Model (HBM) has been coupled with the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) in order to improve hydrographic forecasts in the North and Baltic Seas. The coupled forecast system assimilates satellite sea surface temperature as well as in situ data of temperature and salinity profiles to initialize forecasts up to 5 days. The assimilation uses an ensemble Kalman filter, which dynamically estimates the uncertainty of the state estimate with an ensemble of model states and applies spatially localized updates to improve the ocean state. The structure of the assimilation system, which can analogously be used to extend other forecast models for data assimilation, is discussed. Applying the assimilation reduces errors of the surface temperature by about 0.2 degrees C
Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea
Improvements in turbulence model realizability for enhanced stability of ocean forecast and its importance for downstream components
The numerical stability of ocean circulation models is of high significance in operational forecasting. A substantial improvement in numerical stability of the 3D-ocean model HBM could be achieved by the implementation of new realizability criteria in the turbulence closure scheme. Realizability criteria which were already well documented for closure functions without double diffusion were therefore extended to those using double diffusion. A purely technical validation method called ε-test which is suitable for the detection of numerical stability problems is presented, and the effect of the development in turbulence model is demonstrated under severe weather conditions during extreme storm events. Evaluation of statistics of longer simulations indicate that instabilities appeared only locally and temporary; nevertheless, a significant impact on drift products relying on the current forecasts could be demonstrated, which underlines the importance of realizability in turbulence closure schemes in comprehensive operational model systems including ocean circulation and downstream drift components.Bundesamt für Seeschifffahrt und Hydrographie (BSH) (4225
Improvements in turbulence model realizability for enhanced stability of ocean forecast and its importance for downstream components
<jats:title>Abstract</jats:title><jats:p>The numerical stability of ocean circulation models is of high significance in operational forecasting. A substantial improvement in numerical stability of the 3D-ocean model HBM could be achieved by the implementation of new realizability criteria in the turbulence closure scheme. Realizability criteria which were already well documented for closure functions without double diffusion were therefore extended to those using double diffusion. A purely technical validation method called ε-test which is suitable for the detection of numerical stability problems is presented, and the effect of the development in turbulence model is demonstrated under severe weather conditions during extreme storm events. Evaluation of statistics of longer simulations indicate that instabilities appeared only locally and temporary; nevertheless, a significant impact on drift products relying on the current forecasts could be demonstrated, which underlines the importance of realizability in turbulence closure schemes in comprehensive operational model systems including ocean circulation and downstream drift components.</jats:p>
An operational, assimilative model system for hydrodynamic and biogeochemical applications for German coastal waters
The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas
To improve hydrographic forecasts in the North and Baltic Seas, the HIROMB-BOOS Model (HBM) has been coupled with the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de). The forecast system assimilates satellite sea surface temperature as well as in situ data of temperature and salinity profiles to initialize forecasts up to 5 days. The assimilation uses a fully-featured ensemble Kalman filter, which dynamically estimates the uncertainty of the state estimate with an ensemble model model states and applies spatially localized updates to improve the ocean state. We discuss the structure of the assimilation system, which can analogously be used to extend other forecast models for data assimilation. Further, the forecast improvements caused by the data assimilation are discussed