The Atmospheric and Oceanic Sciences Program at Princeton University, in cooperation with NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL), seeks 2–3 postdoctoral or more senior researchers to join the NOAA Research Global-Nest project. This multi-laboratory project aims to develop ultra-high resolution atmospheric prediction models for better prediction, understanding, and projection of extreme weather events. These include, but are not limited to, atmospheric rivers, tornadic thunderstorm outbreaks, “bomb” cyclones and associated winter storms, landfalling hurricanes, and hydroclimate extremes. Both phenomena and their impacts will be investigated, as will potential changes in these events in warmed climates.
The successful applicants will work with members of the GFDL FV3 Team on the following topics, using the FV3-based GFDL System for High-resolution prediction on Earth-to-Local Domains (SHiELD: www.gfdl.noaa.gov/shield/):
- Analysis, validation, and understanding of the characteristics of simulated extreme weather events, including their physical processes, larger-scale environments, predictability, and impacts.
- Analysis of synoptic-scale, planetary-scale, or surface-atmosphere interactions with mesoscale and convective-scale circulations, especially extreme events.
There will be a particular emphasis upon the development and use of new two-way global-to-regional nested and global storm-resolving configurations of SHiELD designed to reach convective scales (< 5 km) over the contiguous United States and surrounding waters. The principal goals of the applicant’s research will be performing analyses and developing products, and may also involve contributing to model development activities. The project cooperates with the Atlantic Oceanic and Meteorological Laboratory (AOML), National Severe Storms Laboratory (NSSL), and other university and laboratory collaborators. This work also ties directly to development of the Unified Forecast System (UFS) and GFDL Seamless Modeling Suite for weather and climate modeling.
All applicants should have a strong background in using common programming and scripting languages to analyze or otherwise use large meteorological datasets, including numerical model output, field campaign data, reanalysis, and both remote and in-situ observations. Experience with model development and high-performance computing is desirable but not necessary. Applicants with experience in applying machine-learning techniques to research will be given special consideration. Selected applicants will be expected to work in a collaborative environment, adhere to best software and data practices, write technical notes and peer-reviewed publications on their work, and for their work to be prepared for use by coworkers and external collaborators after publication.
These are one year positions with potential for an additional year based on candidate performance and continued funding. Candidates should have a doctoral degree in atmospheric science or a related field. Applicants are asked to submit a CV, publication list, a one-to-two page statement of research interests, and contact information for 3 references. Review of applications will begin as soon as they are received and continue until the position is filled. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. Applicants should apply online at https://www.princeton.edu/acad-positions/position/27661. This position is subject to Princeton University's background check policy.
For more information about the research project and application process, please contact Lucas Harris at firstname.lastname@example.org, Jan-Huey Chen at email@example.com, or Kun Gao at firstname.lastname@example.org. The position is subject to the University’s background check policy.
Princeton University is an Equal Opportunity/Affirmative Action Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.