An Overview of Ensemble Forecasting and Data Assimilation

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

Atmospheric Modeling, Data Assimilation, and Predictability

This comprehensive text and reference work on numerical weather prediction covers for the first time, not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear...

Eakf-cmaq: Development and Initial Evaluation of an Ensemble Adjustment Kalman Filter Based Data Assimilation for Co

An integrated approach to modeling atmospheric chemistry with trace gas data assimilation is a relatively new focus of the atmospheric chemistry modeling community. It is expected that the predictive capability of CTMs can be significantly improved by assimilating measurements of key trace gases from satellite-based platforms and surface monitors. Ensemble adjustment Kalman filter (EAKF) method...

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...

Ensemble Forecast of Analyses: Coupling Data Assimilation and Sequential Aggregation

Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational errors are typically not accounted for. This paper introduces a way to address these limitations by coupling...