DATA FORECASTING USING KALMAN FILTER ' Alessandro GIROLAMI

University of Oklahoma Graduate College Assimilation of Casa and Wsr-88d Radar Data for Tornadic Convective Storms Using an Ensemble Kalman Filter and Applications in Probabilistic Ensemble Forecasting

Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering

Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow foreca...

Time Delay and Data Dropout Compensation in Networked Control Systems Using Extended Kalman Filter

In networked control systems, time delay and data dropout can degrade the performance of the control system and even destabilize the system. In the present paper, the Extended Kalman filter is employed to compensate the effects of time delay and data dropout in feedforward and feedback paths of networked control systems. In the proposed method, the extended Kalman filter is used as an observer ...

Management Working Papers School of Management Forecasting the weekly time-varying beta of UK firms: comparison between GARCH models vs Kalman filter method

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

Storm surge forecasting using Kalman filtering ( a review ) Report 95 - 99

Two data assimilation procedures for real time storm surge forecasting based on Kalman ltering are described. To reduce the computational burden of the Kalman lter, a time invariant lter approximation is suggested rst. This lter is computed using the Chandrasekhar-type algorithm. The resulting data assimilation procedure has been used for storm surge forecasting on a routine basis for a number ...