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. The diagnostics in the tutorial use Matlab®. To configure your environment to use Matlab and the DART diagnostics, see the Configuring Matlab® for netCDF & DART section of the Getting_Started document.

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 10Regression and Nonlinear Effects
Section 11Creating DART Executables
Section 12Adaptive Inflation
Section 13Hierarchical Group Filters and Localization
Section 14Observation Quality Control
Section 15DART Experiments: Control and Design
Section 16Diagnostic Output
Section 17Creating Observation Sequences
Section 18Lost in Phase Space: The Challenge of Not Knowing the Truth
Section 19DART-Compliant Models and Making Models Compliant: Coming Soon
Section 20Model Parameter Estimation
Section 21Observation Types and Observing System Design
Section 22Parallel Algorithm Implementation: Coming Soon
Section 23Location Module Design
Section 24Fixed Lag Ensemble Kalman Smoother (not available yet)
Section 25A Simple 1D Advection Model: Tracer Data Assimilation