NUOPC was formed in 2008 as an agreement to coordinate activities between NWS, the U.S. Navy, and the U.S. Air Force to develop and implement the next-generation National Operational Global Ensemble modeling system. The NUOPC plan consists of the following elements:
- A national operational NWP system with a commitment to address common requirements
- A multicomponent system with interoperable components built upon common standards and a common framework
- Managed ensemble diversity to quantify and bound forecast uncertainty,
- Ensemble products used to drive high-resolution regional/local prediction and other downstream models
- A national research agenda for global NWP to accelerate development and transition to operations
- Increased leverage of partner agencies to avoid independent/duplicative operating costs.
Recognizing that prediction efforts over a longer time scale require more emphasis on research, much of which occurs at agencies not participating in NUOPC, the ESPC interagency effort was established in 2010. Initially, ESPC efforts encompassed the original NUOPC partners, but this was updated in 2013 to include environmental research activities from NASA, DOE, and NSF. This expansion of the ESPC acknowledged the need to improve coordination and collaboration across the entire federally sponsored environmental research and operational prediction community to improve global prediction at the weather-to-climate interface. The partnership pursues the goal of building a seamless prediction capability, to support internally consistent decision products across time scales and agency missions (Hurrell et al. 2009; WMO 2015).
While each agency retains its separate mission needs, the ESPC partnership recognizes that these missions rest on a central core national environmental modeling need for global integrated atmospheric, oceanic, terrestrial, cryospheric, and near-Earth space environment models. While prediction at longer time scales is generally estimated to be beyond the limits of deterministic predictability, multimodel ensemble-based probabilistic techniques provide a means for making meaningful forecasts at longer time scales (NRC 2016).