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| 2 | == Introduction == |
| 3 | |
| 4 | The Weather Research and Forecasting (WRF) modelling system is composed of |
| 5 | several components which need to be executed sequentially. The manual execution |
| 6 | of this workflow is a time-consuming and error-prone task. Thus, it is customary to automate the process to |
| 7 | some degree. However, the automation depends on the experiment to be carried |
| 8 | out since the workflow depends on the experiment. This leads to the development |
| 9 | of specific WRF workflow automation scripts for each experiment. But this is a time consuming task. When the experiment requires running more |
| 10 | than a single model run, the complexity increases and the workflow of the |
| 11 | different runs needs to be taken into account. At this point the problems multiply: the large |
| 12 | number of simulations now require a monitoring system to check their successful |
| 13 | completion; failed runs need to be re-run. If failures are common, the |
| 14 | re-running process also needs to be automated. |
| 15 | |
| 16 | '''WRF4G''' is a flexible |
| 17 | framework to manage the WRF workflow covering a wide range of |
| 18 | simulation experiments composed of multiple runs with different degrees of |
| 19 | dependence. The framework is layered to separate the experiment design from the |
| 20 | execution environment. WRF4G includes a monitoring system and easily restarts |
| 21 | broken simulations until the experiment is completed. |
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