MMM Director's Message

Christopher Davis NCAR Associate Director, MMM photo
Christopher Davis
NCAR Associate Director, MMM

Greetings and welcome to the 2018 MMM Laboratory Annual Report.

A common theme that you will find in the achievements of FY2018 is pioneering studies of atmospheric processes and predictability, and demonstrations of next-generation prediction capabilities. These are in line with NCAR Grand Challenges, NCAR strategic priorities and MMM goals. This includes the increasing convergence of mesoscale and large-eddy simulations. Eddy-resolving simulations of hurricanes are combined with experimental observing technologies to assess realistic levels of turbulence in the hurricane boundary layer. Global, convection-permitting simulations with the Model for Prediction Across Scales (MPAS) have been used to conduct fundamental studies of atmospheric predictability. Furthermore, a regional version of the MPAS now exists and offers a promising route to next-generation computing technologies and coupled regional climate simulations in the next few years.

Studies spanning the mesoscale-to-turbulence-scale regime continue to reveal important new results in this frontier area of research on physical processes. These results include fundamental studies of marine boundary layers subject to underlying rapid variations of sea surface temperature, and the changes of deep convection over land in global warming scenarios. A new parameterization approach based on a Lagrangian treatment of microphysics shows considerable promise. Collectively, the research in fundamental physical processes will be crucial for NCAR’s efforts to conduct research across weather and climate time and space scales. The results of this research will benefit the unification of community modeling as envisioned by the Singletrack project.

Additional work this past year highlights the development of prediction techniques and insights regarding high-impact weather and attendant hazards such as storm surge and hail. This work spans short range prediction of hours to seasonal and decadal time scales. The potential for data assimilation to uncover model biases that hinder prediction capability is being increasingly realized through advanced diagnostics.

I invite you to read more details about our recent accomplishments in the following pages.