In some operational forecast executions, the results have to be delivered at a given time using the available input data at that time, even if not totally complete (initial and boundary conditions, observations, etc.). 

The aim of the weak dependencies feature is to allow the execution of a particular task even if any (not all) of its parent tasks have failed.

As an example, we could have a workflow that runs an ensemble of different air quality models. It could happen that one or more model outputs are missing when the workflow is executed, but anyway we need to run the ensemble with the available ones. The following figure shows an example of how this type of workflow will look in Autosubmit:

\begin{center}
\includegraphics[width=0.8\textwidth]{a408_20240402_1651.png}
\captionof*{figure}{\color{NavyBlue} Example of ensemble workflow using weak dependencies (dashed arrows). Each box represent a task (a single step in the workflow), arrows represent dependencies between jobs, and colors represent the status of each job. In this case, failures didn't prevent subsequent jobs to be completed.}
\end{center}

This feature is used in some of the operational forecast executions performed at BSC-ES, for instance in the Copernicus Atmosphere Monitoring Service (CAMS) air quality ensemble forecast executions done with the in-house MONARCH model. In this case, some contingency checks were added to ensure that the forecast is produced even if some of the input data are not available.
