The DART Tutorial is intended to aid in the understanding of ensemble data assimilation theory and consists of step-by-step concepts and companion exercises with DART.
Before beginning the DART Tutorial, make sure you are familiar with the prerequisite statistical concepts by reading Conditional probability and Bayes’ theorem.
The diagnostics in the tutorial use Matlab®. To learn how to configure your environment to use Matlab and the DART diagnostics, see the documentation for MATLAB observation space diagnostics.
Section 1: Filtering For a One Variable System
Section 2: The DART Directory Tree
Section 3: DART Runtime Control and Documentation
Section 6: Other Updates for An Observed Variable
Section 7: Some Additional Low-Order Models
Section 8: Dealing with Sampling Error
Section 9: More on Dealing with Error; Inflation
Section 10: Regression and Nonlinear Effects
Section 11: Creating DART Executables
Section 12: Adaptive Inflation
Section 13: Hierarchical Group Filters and Localization
Section 14: Observation Quality Control
Section 15: DART Experiments: Control and Design
Section 16: Diagnostic Output
Section 17: Creating Observation Sequences
Section 20: Model Parameter Estimation
Section 21: Observation Types and Observing System Design
Section 22: Parallel Algorithm Implementation: Coming Soon
Section 23: Location Module Design
Section 24: Fixed Lag Smoother (not available yet)