Development and analysis of Global-Nest and Global Storm Resolving Models at Princeton University in Princeton, New Jersey

Job Description:

Position: Development and analysis of Global-Nest and Global Storm Resolving Models

Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks postdoctoral scientists or more senior research scientists to join the NOAA Research Global-Nest Initiative. This multi-laboratory project aims to develop ultra-high resolution atmospheric prediction models for better prediction, understanding, and projection of extreme weather events. The research topics include, but are not limited to: 1) predictability of extreme weather events, e.g. tropical cyclones, winter storms, heatwave, or drought events, in sub-seasonal to seasonal (S2S) time scales; 2) microphysics processes, including warm rain and ice processes, cloud-radiation interactions, and double-moment microphysics, and their impacts on weather systems; 3) data assimilation (DA) and ensemble forecast, e.g. representing model uncertainty, mitigating/correcting systematic model error, and assimilation of surface observations For these research topics, the successful applicants will work with members of the GFDL FV3 Team, using the FV3-based GFDL System for High-resolution prediction on Earth-to-Local Domains (SHiELD: www.gfdl.noaa.gov/shield/), to analyze, validate, and understand the characteristics of model simulated extreme weather events, with the focus on their physical processes, larger-scale environments, predictability, and impacts. There is a particular interest in the analysis of synoptic-scale, planetary-scale, or surface-atmosphere interactions with mesoscale and convective-scale circulations on medium-range to S2S time scales. The principal goals of the applicant's research will be to perform analyses and develop products, and may also involve contributing to model development activities. 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 who are interested in DA research and development should have a strong background in using DA systems (such as GSI or JEDI) and a strong understanding of the data sets and algorithms used in DA systems. Experience with machine learning is also welcome. 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. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. Candidates should have a doctoral degree in atmospheric science or a related field. Complete applications include a cover letter, CV with publication list, a one-to-two page research statement, and 3 letters of recommendation. Review of applications will begin November 30th, 2024, 11:59 PM EST, and will continue until all positions are filled. Applicants should apply online at https://puwebp.princeton.edu/AcadHire/position/36941. For more information about the research project and application process, please contact Jan-Huey Chen at jan-huey.chen@noaa.gov, Linjiong Zhou at linjiong.zhou@noaa.gov, Mingjing Tong at mingjing.tong@noaa.gov, or Lucas Harris at lucas.harris@noaa.gov. The work location for this position is in-person on campus at Princeton University. This position is subject to the University's background check policy.

PI253820110

Salary:

$1.00

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