Introduction

Statement of purpose

The purpose of this package is to provide a research framework for the theoretical development and empirical validation of novel data assimilation techniques. While analytical proofs can be derived for classical methods, such as the Kalman filter in linear-Gaussian dynamics, most currently developed DA techniques are designed for estimation in nonlinear, non-Gaussian models where no analytical solution typically exists. Rigorous validation of novel data assimilation methods, therefore, must be performed with reproducible numerical simulations in standard test-cases in order to demonstrate the effectiveness and computational performance of the proposed technique. Pursuant to proposing a novel DA method, one should likewise compare its performance with other standard methods within the same class of estimators.

This package implements a variety of standard data assimilation algorithms, including some of the widely used performance modifications that are used in practice to tune these estimators. Standard libraries exist for full-scale DA system research and development, e.g., the Data Assimilation Research Testbed (DART), but there are fewer standard options for theoretical research and algorithm development in simple test systems. Many basic research frameworks, furthermore, do not include standard operational techniques developed from classical VAR methods, due to the difficulty in constructing tangent linear and adjoint codes. DataAssimilationBenchmarks.jl provides one framework for studying sequential filters and smoothers that are commonly used in online, geoscientific prediction settings, including ensemble estimators, classical VAR techniques (currently in-development) and (in-planning) hybrid-EnVAR methods.

Validated methods

For a discussion of many of the following methods and benchmarks for their performance validation, please see the manuscript Grudzien et al. 2022.