COMMUNITY ENSEMBLE KALMAN FILTER SYSTEM
Welcome to the users page for the Community Ensemble Kalman Filter (EnKF) system. The community EnKF system is a Monte-Carlo algorithm for data assimilation that uses an ensemble of short-term forecasts to estimate the background-error covariance in the Kalman Filter. It is designed to be flexible, state-of-art, and run efficiently on various parallel computing platforms. The EnKF system is in the public domain and is freely available for community use.
The Developmental Testbed Center (DTC) maintains and supports a community version of the EnKF system (currently Version 1.3) as well as the community version of the Grid-point Statistical Interpolation (GSI) (now at Version 3.7). The testing and support of this EnKF system at the DTC includes both regional numerical weather prediction (NWP) applications coupled with the Weather Research and Forecasting (WRF) Model, as well as global NWP applications.
The EnKF uses the observation operators in the GSI system to transform model variables to observed variables in observation space. Therefore, the types of observations available for use in the EnKF match those for the GSI. EnKF Version 1.3 has been tested to work with the GSI Version 3.7. For a complete list of the new functions and changes included in the latest release version, please check EnKF User's Guide section 1.3.