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Bergen, Norway

Skjervheim J.-A.,Statoil | Evensen G.,Statoil | Evensen G.,Mohn Sverdrup Center
Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011 | Year: 2011

This paper compares two ensemble-based data-assimilation methods when solving the history-matching problem in reservoir-simulation models. The methods are the Ensemble Kalman Filter (EnKF) and the Ensemble Smoother (ES). EnKF has been used extensively in petroleum applications while ES is now used for the first time for history matching. ES differs from EnKF by computing a global update in the space-time domain, rather than using recursive updates in time as in EnKF. Thus, the sequential updating of the realizations with associated restarts is avoided. EnKF and ES provide identical solutions for linear dynamical models. However, for nonlinear dynamical models, and in particular models with chaotic dynamics, EnKF is superior to ES, due to the fact that the recursive updates keep the model on track and close to the true solution. Thus, ES is not much used and EnKF has been the method of choice in most data assimilation studies where ensemble methods are used. On the other hand, reservoir simulation models are rather diffusive systems when compared to the chaotic dynamical models that were previously used to test ES. If we can assume that the model solution is stable with respect to small perturbations in the initial conditions and the history-matching parameters, then ES should give similar results to EnKF, and ES may be a more efficient and much simpler method to implement and apply. In this paper we compare EnKF and ES and show that ES indeed provide for an efficient ensemble-based method for history matching. Copyright 2011, Society of Petroleum Engineers, Inc. Source

Wan L.,National Marine Environmental Forecasting Center | Bertino L.,Mohn Sverdrup Center | Zhu J.,CAS Institute of Atmospheric Physics
Journal of Atmospheric and Oceanic Technology | Year: 2010

The ensemble Kalman filter (EnKF) has proven its efficiency in strongly nonlinear dynamical systems but is demanding in its computing power requirements, which are typically about the same as those of the fourdimensional variational data assimilation (4DVAR) systems presently used in several weather forecasting centers. A simplified version of EnKF, the so-called ensemble optimal interpolation (EnOI), requires only a small fraction of the computing cost of the EnKF, but makes the crude assumption of no dynamical evolution of the errors. How do both these two methods compare in realistic settings of a Pacific Ocean forecasting system where the computational cost is a primary concern? In this paper the two methods are used to assimilate real altimetry data via a Hybrid Coordinate Ocean Model of the Pacific. The results are validated against the independent Argo temperature and salinity profiles and show that the EnKF has the advantage in terms of both temperature and salinity and in all parts of the domain, although not with a very striking difference. © 2010 American Meteorological Society. Source

Chen L.,CAS Institute of Atmospheric Physics | Chen L.,Mohn Sverdrup Center | Johannessen O.M.,Mohn Sverdrup Center | Wang H.,CAS Institute of Atmospheric Physics | Ohmura A.,ETH Zurich
Advances in Atmospheric Sciences | Year: 2011

Annual precipitation, evaporation, and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet (GrIS), based on the common period of 1989-2001. The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses. Further, ERA-interim showed the closest spatial distribution of accumulation to the observation. Concerning temporal variations, ERA-interim showed the best correlation with precipitation observations at five synoptic stations, and the best correlation with in situ measurements of accumulation at nine ice core sites. The mean annual precipitation averaged over the whole GrIS from ERA-interim (363 mm yr-1) and mean annual accumulation (319 mm yr-1) are very close to the observations. The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed. © 2011 China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg. Source

Hannart A.,Pab. II | Carrassi A.,Mohn Sverdrup Center | Bocquet M.,ParisTech National School of Bridges and Roads | Ghil M.,Ecole Normale Superieure de Paris | And 4 more authors.
Climatic Change | Year: 2016

We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers. © 2016 Springer Science+Business Media Dordrecht Source

Melsom A.,Norwegian Meteorological Institute | Counillon F.,Mohn Sverdrup Center | LaCasce J.H.,University of Oslo | Bertino L.,Mohn Sverdrup Center
Ocean Dynamics | Year: 2012

We investigate trajectory forecasting as an application of ocean circulation ensemble modeling. The ensemble simulations are performed weekly, starting with assimilation of data for various variables from multiple sensors on a range of observational platforms. The ensemble is constructed from 100 members, and member no. 1 is designed as a standard (deterministic) simulation, providing us with a benchmark for the study. We demonstrate the value of the ensemble approach by validating simulated trajectories using data from ocean surface drifting buoys. We find that the ensemble average trajectories are generally closer to the observed trajectories than the corresponding results from a deterministic forecast. We also investigate an alternative model in which velocity perturbations are added to the deterministic results and ensemble mean results, by a first-order stochastic process. The parameters of the stochastic model are tuned to match the dispersion of the ensemble approach. Search areas from the stochastic model give a higher hit ratio of the observations than the results based on the ensemble. However, we find that this is a consequence of a positive skew of the area distribution of the convex hulls of the ensemble trajectory end points. © Springer-Verlag 2012. Source

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