EnKF-C user guide

TL;DR

EnKF-C provides a practical, scalable framework for offline ensemble data assimilation in large, layered geophysical models, supporting EnKF, EnOI, and hybrid configurations across multiple grids. It integrates core EnKF theory with a three-stage workflow (PREP, CALC, UPDATE), localization, asynchronous data assimilation, and various ensemble-transform schemes (ETKF, DEnKF) to efficiently update ensemble fields. The guide details parameterization, observation preprocessing, local transforms, and diagnostics, offering concrete guidance for configuring, tuning, and validating DA systems in ocean and climate contexts. By combining flexible grid handling, memory-conscious design, and robust tuning options (R-factors, inflation, K-factor, LA, and hybrid covariance), EnKF-C enables operational-scale DA with practical guidance and example configurations like OceanMAPS.

Abstract

EnKF-C provides a compact generic framework for off-line data assimilation into large-scale layered geophysical models with the ensemble Kalman filter (EnKF). It is coded in C for GNU/Linux platform and can work either in EnKF, ensemble optimal interpolation (EnOI), or hybrid (EnKF/EnOI) modes.

Figures (4)

• Figure 1: Data assimilation cycle of the Kalman filter.
• Figure 2: The principle diagram of EnKF-C workflow in EnKF mode.
• Figure 3: Parameter files in EnKF-C.
• Figure 4: Example of observation timing in a MOM based ocean forecasting system.