CIRES/NOAA GSD Numerical Weather Prediction Scientist

The Cooperative Institute for Research in Environmental Sciences (CIRES) has an immediate opening for a junior research scientist.


Date posted

Sep. 16, 2019 12:00 am

Application deadline

Nov. 16, 2019 12:00 am


The Cooperative Institute for Research in Environmental Sciences (CIRES)


  • United States

Job description

CIRES/NOAA GSD Numerical Weather Prediction Scientist

Requisition Number: 19756

Location: Boulder, Colorado

Employment Type: Research Faculty

Schedule: Full-Time

Job Summary

The Cooperative Institute for Research in Environmental Sciences (CIRES) has an immediate opening for a junior research scientist with background in Numerical Weather Prediction (NWP) and meteorological analysis to work in the NOAA Earth System Research Laboratory (ESRL)/Global Systems Division (GSD).  The scientist will help enhance transition of innovations in NWP to NOAA’s operational production suite by supporting community scientists in using and developing global and regional configurations of NOAA’s Unified Forecast System, which uses the Finite Volume Cubed Sphere (FV3) dynamical core. The scientist will also work on NWP development projects of ESRL/GSD. The work will be conducted in collaboration with other scientists in ESRL/GSD, and involve collaborations with the Developmental Testbed Center, Global Model Test Bed and other groups at NOAA Laboratories, NCAR, NCEP and the scientific community. The position is located in Boulder, Colorado.  Salary will be commensurate with experience.

Who We Are

At CIRES, the Cooperative Institute for Research in Environmental Sciences, more than 800 environmental scientists work to understand the dynamic Earth system, including people’s relationship with the planet. CIRES is a partnership of NOAA and the University of Colorado Boulder, and our areas of expertise include weather and climate, changes at the Earth’s poles, air quality and atmospheric chemistry, water resources, and solid Earth sciences. Our vision is to be instrumental in ensuring a sustainable future environment by advancing scientific and societal understanding of the Earth system.


Participate in the activities of the Developmental Testbed Center (DTC), including its Global Model Test Bed (GMTB)
  • Contribute to model development for creation of new outputs and diagnostics aimed at facilitating development of physical parameterization.
  • Use forecast verification and diagnostic methods to identify and document strengths and weaknesses in UFS global and regional forecasts.
  • Develop meteorological case studies to exemplify and document model strengths and weaknesses.
  • Collaborate with model developers from NOAA and partnering organization to use case studies to investigate potential improvements to the model, particularly in the area of model physical parameterizations.
  • Conduct testing and evaluation of model innovations, particularly in the area of model physics.
  • Support collaborators in adding their innovations to the code using well-established protocols.
  • Contribute to preparation and delivery of educational materials such as documentation and tutorials.
Engage in additional activities in NWP related to ongoing projects in GSD, including both regional and global modeling
  • The applicant will be expected to work collaboratively with GSD and DTC partners, including the National Center for Atmospheric Research, the NOAA National Centers for Environmental Prediction (NCEP), and the research community.  She/he will also work with colleagues to document and present the results in the form of journal articles, seminars, and conference presentations.


  • M.S. or Ph.D. degree in atmospheric or related science.
  • Experience with NWP.
  • Experience with meteorological analysis.
  • Robust computational skills and experience in one or more programming languages.
  • Excellent oral and written communication skills in English.
  • Ability to work both independently and in a team environment.
  • Availability to travel at the level of 2-3 trips/year.

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