Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the bias-corrected ensemble members, where the weights reflect the relative skill of the individual members over a training period. This wor...

In this paper we propose a way to integrate data at different spatial scales using the ensemble Kalman filter (EnKF), such that the finest scale data is sequentially estimated, subject to the available data at the coarse scale (s), as an additional constraint. Relationship between various scales has been modeled via upscaling techniques. The proposed coarse-scale EnKF algorithm is recursive and...

Methods of data assimilation are established in physical sciences and engineering for the merging of observed data with dynamical models. When the model is nonlinear, methods such as the ensemble Kalman filter have been developed for this purpose. At the other end of the spectrum, when a model is not known, the delay coordinate method introduced by Takens has been used to reconstruct nonlinear ...

The Ensemble Kalman Filter (EnKF) has been successfully applied in petroleum engineering during the past few years to constrain reservoir models to production or seismic data. This sequential assimilation method provides a set of updated static variables (porosity, permeability) and dynamic variables (pressure, saturation) at each assimilation time. However, several limitations can be pointed o...

The Ensemble Kalman Filter (EnKF), in its native formulation as originally introduced by Evensen (1994) and Burgers et al. (1998), has used pure Monte Carlo sampling when generating the initial ensemble, the model noise and the measurement perturbations. This has been a useful approach since it has made it very easy to interpret and understand the method (see Evensen, 2003). Further, sampling e...

Title of dissertation: ERRORS IN THE INITIAL CONDITIONS FOR NUMERICAL WEATHER PREDICTION: A STUDY OF ERROR GROWTH PATTERNS AND ERROR REDUCTION WITH ENSEMBLE FILTERING John Harlim, Doctor of Philosophy, 2006 Dissertation directed by: Professor Brian R. Hunt Department of Mathematics In this dissertation, we study the errors of a numerical weather prediction due to the errors in initial condition...

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase ...

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of weather and wildfire behavior from real-time weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in timespace with a highly parallel implementation. In this paper, we ...