Numerical Weather Prediction (NWP) Research Scientist

  • Full Time
  • Norman

Overview:
The Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma is seeking
a research scientist or research associate to participate in research of cutting-edge numerical
weather prediction (NWP) and ensemble weather prediction to improve forecasts of high impact
weather, including NOAA-funded projects on flash flood prediction, winter weather predictions,
and the design and evaluation of the next-generation Rapid Refresh Forecast System (RRFS)
ensemble for severe storm forecasting. These projects utilize NOAA’s next generation FV3-LAM
model as part of the Unified Forecast System.
Job Responsibilities:
• Set up and maintain CAPS forecast systems, including coordinating the implementation
of new and updated model capabilities for research and realtime forecasting.
• Perform real-time ensemble NWP forecasts using FV3-LAM to support CAPS research
projects. Some remote evening and nighttime work may be required during special
operating periods.
• Contribute to the objective verification of CAPS ensemble FV3-LAM forecasts.
• Attend professional meetings and conferences to present research results, and publish
findings in peer-reviewed academic journals.
Required Qualifications:
• A Ph.D. (to be appointed as a post-doc or research scientist) or Master’s degree (for a
research associate appointment) in meteorology, atmospheric science, or a closelyrelated field.
• At least one year of experience working with numerical weather prediction models (e.g.,
WRF, FV3, ARPS) in a Linux environment.
• At least three years of experience in scientific programming and computing on Linux
systems using high-level programming languages (such as Fortran and/or Python).
• Ability to work independently and troubleshoot issues in community modeling software
packages.
• Ability to communicate effectively in meetings, research presentations, and formal
publications.
Desired Skills:
• Ability to manage complex computing tasks on high-performance computing systems.
• Experience with developing or modifying workflow automation using, e.g., Linux/Unix
shell scripts, Python, and/or Rocoto.
• Familiarity with methods and software for NWP forecast visualization, evaluation, and
verification, such as Python plotting packages, Model Evaluation Tools (MET), etc.
• Familiarity with accessing and downloading weather data via platforms such as AWS
and/or UNIDATA LDM.

To apply for this job email your details to kbrewster@ou.edu