Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes. To identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding as well as large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. We present an online diagnostic tool that addresses this challenge by monitoring model fields in a generic manner as they evolve within the time integration cycle.
The tool is convenient to use. It allows the users to select and monitor sampling conditions and quantities of interest (QoIs) by specifying namelist variables. Both the evolving values of the QoIs and their increments caused by different atmospheric processes can be monitored and archived. Vertical integrals are diagnosed upon request. Multiple sampling conditions can be monitored in a single simulation, and unconditional sampling is also supported.
The tool has been designed for and implemented in the atmosphere component of the Energy Exascale Earth System Model (E3SM) version 1. It is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.