# DART Tutorial¶

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 Configuring Matlab® for netCDF & DART.

**Section 1:**Filtering For a One Variable System**Section 2:**The DART Directory Tree**Section 3:**DART Runtime Control and Documentation**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