POST-DOC: Coupled land-atmosphere model performance

The Center for Ocean-Land-Atmosphere Studies (COLA) at George Mason University is expecting to hire a post-doc to start in May or June 2020 to conduct land surface and coupled land-atmosphere model evaluation and validation as part of the development of the Unified Forecast System (UFS; https://ufscommunity.org/). UFS is a community-based, coupled comprehensive Earth system model that will assume much of the operational forecasting duties for the National Weather Service in the near future. This is a 2-year project.

Among the many new and updated model components are the land surface model (LSM), many parameterizations of atmospheric physics and the dynamical core. At COLA, we will be evaluating implementations of the Noah-MP LSM and possibly other models, their stand-alone performance, and especially coupled performance in terms of process-based land-atmosphere coupling metrics that can inform model development and calibration. The goal is to improve and optimize forecast model performance on time scales from sub-daily to subseasonal, ensuring UFS properly captures coupled land-atmosphere processes by which predictability inherent in the slowly-varying land states can be harvested.   

We seek Spring 2020 or other recent recipients of a PhD degree in atmospheric science, hydrology, civil engineering, geography, ecology, or related fields who have expertise in land-atmosphere coupling and related data analysis. Experience in numerical modeling is not necessary, but helpful. Familiarity with using large data sets and strong computational skills are essential.

The incumbent will be based at COLA and will interact closely with colleagues at the Environmental Modeling Center and the National Center for Atmospheric Research, among others, to diagnose model behavior and skill, and to inform model development. Applicants should contact me directly <pdirmeye@gmu.edu> with a current CV and list of references, along with a one-page summary of interest and appropriate skills.