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 :doc:`conditional-probability-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 :doc:`/guide/matlab-observation-space`.
- Section 1: Filtering For a One Variable System
- Section 2: The DART Directory Tree
- Section 3: DART Runtime Control and Documentation
- Section 4: How should observations of a state variable impact an unobserved state variable? Multivariate assimilation.
- Section 5: Comprehensive Filtering Theory: Non-Identity Observations and the Joint Phase Space
- 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 18: Lost in Phase Space: The Challenge of Not Knowing the Truth
- Section 19: DART-Compliant Models and Making Models Compliant: Coming Soon
- 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)
- Section 25: A Simple 1D Advection Model: Tracer Data Assimilation