MMM Director's Message

MMM Director
Christopher Davis
MMM Director

Greetings and welcome to the 2019 MMM Laboratory Annual Report.

During the past year MMM has been engaged in research spanning a wide range of space and time scales, with an emphasis on cross-lab collaboration and interdisciplinarity. Projects such as boundary layer integration, cross-lab data assimilation, and the System for Integrated Modeling of the Atmosphere (SIMA), are all examples where MMM has a major role. A boundary-layer working group has been established, comprising five labs, with a focus on integrating observations and models. Data assimilation research has produced a new prototype data assimilation and prediction capability for relocatable domains, as well as improved results for short-range prediction on the convective scale. Work on SIMA, and related work on the unification of the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS) has steadily aligned model development efforts within MMM and across labs. Specifically, MMM staff are working to implement the Community Physics Framework that will allow easier exchange of physics among different dynamical cores. Numerous developments of MPAS have occurred in this past year, including release of a regional version (now being compared with WRF), a prototype deep atmosphere version, a successful port of the code to GPU architectures in collaboration with the NCAR CISL and The Weather Company of IBM, and a data assimilation capability based on the Joint Environmental Data Assimilation Initiative (JEDI).

Science advances mirror the technical advances in terms of their span across scales from planetary to microscale studies. Global convection-permitting modeling of tropical cyclones has examined predictability and the interaction with tropical waves. Mesoscale dynamics of tropical cyclone formation and intensity change has clarified the role of column humidity the deleterious effects of its disruption by vertical wind shear. Fine-scale observations of the boundary layer of intense hurricanes have been collected with an unmanned aircraft system (collaboration with NOAA). Impacts from tropical cyclones has also been an active area of work, including the use of probabilistic coastal inundation information for decision making, and potential impacts from landfalling TC in future climate scenarios.

Predictability and processes have been examined more generally across a variety of scales, from the influence of stochastic processes on ENSO, down to predictability of severe convective events using ensemble forecast techniques coupled with machine learning. Further down into the microscale, research has investigated the fundamental processes of convective initiation in which turbulent entrainment plays a central role. Novel processing techniques using machine learning have enabled interrogation of the three-dimensional attributes of ice crystals in order to resolve the particle size distribution of ice that has proven elusive.

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