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Measuring stiffness of soils in situ
Published in Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto, Computer Methods and Recent Advances in Geomechanics, 2014
Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto
Baù D., M. Ferronato, G. Gambolati and P. Teatini (2002) Basin scale compressibility of the Northen Adriatic by the radioactive marker technique, Geotechnique, 52 , 605-616. Cedizaz. Underground Gas Storage in the World (2013). Evensen, G. (2009). Data Assimilation: The Ensemble Kalman Filter. Springer.
Low-Grid-Resolution-RANS-Based Data Assimilation of Time-Averaged Separated Flow Obtained by LES
Published in International Journal of Computational Fluid Dynamics, 2022
Masamichi Nakamura, Yuta Ozawa, Taku Nonomura
An ensemble Kalman filter, which minimises the error covariance matrix in the same way as a Kalman filter, is used as the method of data assimilation. An ensemble Kalman filter is an improved algorithm that can be applied to nonlinear phenomena such as flow fields. The ensemble Kalman filter optimises the state vector as described by Equation (4) by using a weight function, called Kalman gain, given by Equation (3) as where and represent the error covariance matrix of the state vector and the error covariance matrix of the observation noise , respectively. Here, the superscripts f and a represent prior and posterior distributions.
Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting
Published in Inland Waters, 2022
Cayelan C. Carey, Whitney M. Woelmer, Mary E. Lofton, Renato J. Figueiredo, Bethany J. Bookout, Rachel S. Corrigan, Vahid Daneshmand, Alexandria G. Hounshell, Dexter W. Howard, Abigail S. L. Lewis, Ryan P. McClure, Heather L. Wander, Nicole K. Ward, R. Quinn Thomas
The daily data assimilation for FLARE includes 5 major steps that are triggered each morning (Fig. 1; described in detail by Thomas et al. 2020a). First, FCR water temperature and DO concentrations are simulated by GLM-AED for the preceding 24 h on an hourly time step, using observed meteorology and inflow data as model inputs. The model run is composed of 441 ensemble members, or individual iterations of the GLM-AED model, which differ slightly in their initial conditions and their parameters based on the outcome of prior data assimilation. Second, random noise is added to the model states (i.e., temperature and DO at each depth) for each ensemble member to represent process uncertainty. Third, the ensemble model output is then compared with the most recent observational data from the temperature and DO sensors using an ensemble Kalman filter. The ensemble Kalman filter statistically combines the model ensemble predictions and the observations to adjust the model states and the model parameters to be consistent with the observations. Fourth, the adjusted model states and model parameters for each GLM-AED ensemble are then used as initial conditions and model parameters for a 16-day forecast into the future, using meteorological forecasts from the US National Oceanic and Atmospheric Administration (NOAA) as driver data. The 16-day forecast includes the key sources of uncertainty for the system, including driver data uncertainty of both future weather and inflows. Finally, the outputs from the ensemble forecast are automatically processed to create visualizations, which are emailed to water managers every morning (Fig. 2). The ensemble output is also archived for future analysis in a GitHub repository for versioning control. We refer interested readers to Thomas et al. (2020a) for detailed information on FLARE setup and performance in forecasting thermal structure; here, we focus on DO forecasting and its application for management.