Workshop on Next-Generation Parameterizations of Moist Processes



NOAA and the Development Test Center (DTC) hosted a workshop to stimulate the development of moist process parameterization for the Next-Generation Global Prediction System (NGGPS) and similar global models. The theme of this workshop was also highly relevant to current and future generation regional and mesoscale models. The goal of the workshop was to inform and advise on the future directions of moist process parameterization development, with a particular emphasis on numerical weather prediction applications for scales and resolutions ranging from synoptic- to convection-permitting. The workshop reviewed the representation of processes in current NWS models, surveyed the range of conceptual and theoretical ideas emerging from modeling centers and universities, and worked to develop a medium-term plan to implement and test the most promising ideas in existing and emerging NOAA global and regional forecast models. The workshop included observational, modeling and theoretical perspectives. Specific foci included:

  • Cloud microphysics, especially ice and mixed-phase processes, with emphasis on finding the appropriate level of complexity and numerical formulation of microphysics
  • Efficient and consistent treatments of sub-grid heterogeneity across a range of model resolutions
  • Robust treatment of uncertainties associated with physical parameterization of moist processes (stochastic physics)
  • Interactions of turbulence, radiation, shallow and deep convection, clouds and microphysics
  • Particular challenges raised by parameterizations in the simulation of high-impact events (e.g. hurricanes)

External speakers included Gilbert Brunet (CMC), Chris Bretherton (University of Washington), Richard Forbes (ECMWF), Axel Seifert (DWD), Michael Whitall (UK Met Office).

Related NGGPS Overview

The NOAA's National Weather Service and Office of Oceanic and Atmospheric Research are embarking on an ambitious plan to develop a Next-Generation Global Prediction System (NGGPS). The model at the center of this system is expected to have a spatial resolution of roughly 10 km with the possibility of resolution varying by an order of magnitude or more - similar to mesh sizes used by current limited-area models. While increasing spatial resolution is known to benefit many aspects of forecasts, the global modeling community has relatively little experience with parameterizations at such scales and essentially no experience in developing parameterizations for grids with variable size. The NGGPS also seeks a robust treatment of model uncertainty to enable situation-specific "error bars" around each forecast element; the expectation is that uncertainty estimates will be provided directly by parameterizations wherever feasible.

The parameterization of moist processes including clouds, shallow and deep convection, microphysics, and the coupling with turbulence, is one of the toughest tasks in developing a system like the NGGPS. Clouds are intimately linked to weather through their effects on circulations and energetics and the parameterization of moist processes has historically been one of the toughest challenges for global numerical weather prediction models. This parameterization problem is perhaps most acutely difficult at 1-10 km scales since clouds and convection are neither explicitly resolved nor easily parameterized using traditional assumptions that rely on the presence of many clouds within each grid column.

The NGGPS, therefore, requires a new generation of physical parameterizations to better represent microphysics, clouds, shallow and deep convection, and interactions with turbulence and radiation. Such parameterizations will need to have greater fidelity than current methods across a range of scales and resolutions, and will allow for the representation and propagation of state-dependent uncertainty.


Contacts - Organizing Committee

  • Robert Pincus (CU) (Robert.Pincusinsert
  • James Doyle (NRL) (James.Doyleinsert
  • Yu-Tai Hu (EMC) (yu-tai.houinsert
  • Jamie Wolff (DTC) (jwolffinsert