The Common Community Physics Package (CCPP) is designed to facilitate the implementation of physics innovations in state-of-the-art atmospheric models, the use of various models to develop physics, and the acceleration of transition of physics innovations to operational NOAA models.
Ideas for this project originated within the Earth System Prediction Capability (ESPC) physics interoperability group, which has representatives from the US National Center for Atmospheric Research (NCAR), the Navy, National Oceanic and Atmospheric Administration (NOAA) Research Laboratories, NOAA National Weather Service, and other groups. Physics interoperability, or the ability to run a given physics suite in various host models, has been a goal of this multi-agency group for several years. An initial mechanism to run the physics of NOAA’s Global Forecast System (GFS) model in other host models was developed by the NOAA Environmental Modeling Center (EMC) and later augmented by the NOAA Geophysical Fluid Dynamics Laboratory (GFDL). The CCPP expanded on that work by meeting additional requirements put forth by NOAA, and brought new functionalities to the physics-dynamics interface. Those include the ability to choose the order of parameterizations, to subcycle individual parameterizations by running them more frequently than other parameterizations, and to group arbitrary sets of parameterizations allowing other computations in between them (e.g., dynamics and coupling computations).
The CCPP-physics contains the parameterizations and suites that are used operationally in the UFS Atmosphere, as well as parameterizations that are under development for possible transition to operations in the future. The CCPP aims to support the broad community while benefiting from the community. In such a CCPP ecosystem, the CCPP can be used not only by the operational centers to produce operational forecasts, but also by the research community to conduct investigation and development. Innovations created and effectively tested by the research community can be funneled back to the operational centers for further improvement of the operational forecasts.
Copyright © 2019. All rights reserved.