Climate and weather model development requires ongoing improvements in the representation of a growing list of physical processes. Process-oriented diagnostics (PODs) seek to give insight into the physical mechanisms needed to guide model development. The Model Diagnostics Task Force package (MDTF Diagnostics) is an open-source Python-based unified framework that runs process-oriented diagnostics (PODs) on weather and climate model data. The software package promotes the development and integration of diagnostics by subject matter experts across government, academia, and the private sector to improve the understanding of underlying processes in models under development by NOAA-GFDL and NCAR.
In this talk, we will provide an overview of the MDTF framework, encompassing both the technical and the scientific aspects. The technical overview will lay out the design goals and vision, encompassing key aspects such as Continuous Integration (CI), cloud computing and containerization to further strengthen collaborative development and foster community engagement. A science blurb centered around the Madden–Julian oscillation (MJO) process diagnostics and mesoscale convective systems based on GFDL simulations will be highlighted.
