OpenDA: Integrating models and observations. OpenDA is an open interface standard for (and free implementation of) a set of tools to quickly implement data-assimilation and calibration for arbitrary numerical models.

OpenDA is a generic environment for data-assimilation tasks like parameter calibration and measurement filtering. It provides a platform that allows an easy interchange of algorithms and models. It is a modular framework, containing methods and tools that can be used for a wide range of applications.

Run the OpenDA application with Dud.oda as the main configuration file in the mode that you prefer (with or without GUI). In GUI mode, you can get a real-time update of the execution by checking either Control, Output, or Cost Function tabs. Check the results.

OpenDA supports the method of steady-state Kalman filtering where the \(K\) matrix is computed in a previous run using, for instance, EnKF. This can be considered a special form of optimal interpolation.

All native components of OpenDA (parts of the source code that need to be compiled to a specific platform (in this case Linux)) are compiled for both 32-bit and 64-bit versions of Linux.

Introduction to OpenDA; Introduction to data assimilation; Getting started. OpenDA installation; Example configurations; Step-by-step application setup; OpenDA course; Additional information. NetCDF data objects; Localization; Continue on a previous OpenDA run; Contributing to the source. Developing the Java source

This section contains miscellaneous publications and presentations related to OpenDA. Click an article to download it in PDF format. Case studies for a general audience: Improvement of the tide model for Singapore; Monitoring Air Quality over the …

On operational systems, OpenDA will run repetitively on a scheduled basis, for instance, daily. For each run, there will be new data for new predictions, for instance, for the next day. The start time of the predicted period will then most likely be the end time of the previous run.

The Sequential simulation algorithm in OpenDA is a useful tool for creating your twin experiment. Simulation algorithms OpenDA implements several algorithms that can be used to gradually grow from a simulation model to a data-assimilation system.

In OpenDA, we have two different options for localization: Hamill localization (commonly used) and Zhang localization (no need to implement masks). Example configurations can be found in model_dflowfm_blackbox\tests\dcsmv5_kalman_rst\algorithm\Enkf_localization.xml , and