We investigate by numerical experiment the use of discrete-time stochastic parametrization to account for model error due to unresolved scales in ensemble Kalman filters. The parametrization quantifies the model error and produces an improved non-Markovian forecast model, which generates high-quality forecast ensembles and improves filter performance. We compare this with the methods of dealing...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

Amajor problem in forecasting is estimating the time of some future event. Traditionally, forecasts are designed to minimize an error cost function that is evaluated once, possibly when the event occurs and forecast accuracy can be determined. However, in many applications forecast error costs accumulate over time, and the forecasts themselves may be updated with information that is collected a...

Goodness-of-t is the most popular criterion for neural network time series forecasting. In the context of nancial time series forecasting, we are not only concerned at how good the forecasts t their targets, but we are more interested in proots. In order to increase the forecastability in terms of proot earning, we propose a proot based adjusted weight factor for backpropagation network trainin...

The potential of high-density observations is studied in a practical context of the 4DVAR assimilation. A series of observing system simulation experiments (OSSEs) are carried out. Observations with both uncorrelated and correlated observation errors are simulated in sensitive areas. The results show that: for the observations with uncorrelated error, increasing the observation density generall...

1. Introduction Since its first introduction by Evensen (1994), the ensemble Kalman filter (EnKF) technique for data assimilation has received much attention. Rather than solving the equation for the time evolution of the probability density function of model state, the EnKF methods apply the Monte Carlo method to estimate the forecast error statistics. A large ensemble of model states are inte...

Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast ha...

Title of dissertation: INFORMATION SYNTHESIS ACROSS SCALES IN ATMOSPHERIC STATE ESTIMATION: THEORY AND NUMERICAL EXPERIMENTS Matthew Kretschmer, Doctor of Philosophy, 2015 Dissertation directed by: Professor Edward Ott Department of Physics This thesis studies the benefits of simultaneously considering system information from different sources when performing ensemble data assimilation. In part...

It is often documented, based on autocorrelation, variance ratio and power spectrum, that exchange rates approximately follow a martingale process. Because autocorrelation, variance ratio and spectrum check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insigniÞcant. In this pa...