26-118: Ensemble Prediction Researcher

Colorado State University•Boulder, CO

3d•$95,000 - $125,000•Hybrid

About The Position

The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU) is seeking a full-time Research Scientist to conduct collaborative research with the National Oceanic and Atmospheric Administration (NOAA) at the Global Systems Laboratory (GSL) in Boulder, CO. This role will be within the NOAA/OAR/GSL Earth Prediction Advancement Division (EPAD) Physics Branch. CIRA is a research organization focused on interdisciplinary atmospheric sciences, leveraging advances in engineering and computer science to assist NOAA, CSU, Colorado, and the Nation through applied research. GSL provides environmental observation, prediction, computer, visualization, and information systems to the National Weather Service and the nation, delivering forecasts and predictions from minutes to weeks away. GSL is a leader in applied research, development, and technology transfer of environmental data, models, products, and services to enhance environmental understanding and support commerce, protect life and property, and promote a scientifically literate public.

Requirements

Bachelor’s Degree in Atmospheric Science or related field plus 10 years of relevant work experience, OR Master’s Degree in Atmospheric Science or related field plus 5 years of relevant work experience, OR PhD in Atmospheric Science plus 2 years of relevant work experience (for RA IV classification).
Bachelor’s Degree in Atmospheric Science or related field plus 5 years of relevant work experience, OR Master’s Degree in Atmospheric Science or related field plus 2 years of relevant work experience, OR a recent PhD in Atmospheric Science (for RA III classification).
Experience with ensemble weather prediction systems, including knowledge of their strengths and weaknesses for balancing probabilistic skill with appropriate forecast dispersion.
At least two years of experience in developing, running, and evaluating numerical weather prediction model experiments.
At least two years of experience in Fortran and/or C++, Python, high-performance computing, and Linux computing environments.
Experience communicating scientific results, such as through conference presentations, seminars, technical reports, and/or publications.

Nice To Haves

Experience with designing, running, and/or evaluating the output from ensemble numerical weather prediction systems.
Understanding of methods for verifying ensemble/probabilistic weather prediction forecasts.
Understanding of state-of-the-art regional (particularly thunderstorm-resolving) and/or global numerical weather prediction models.
Familiarity with artificial intelligence applications for weather prediction, particularly those that can provide probabilistic forecast information.
Experience using and contributing to community modeling systems.
Experience with NOAA’s Unified Forecast System (UFS), the Weather Research and Forecasting (WRF) model, and/or the Model for Prediction Across Scales (MPAS).

Responsibilities

Contribute to the development of next-generation ensemble (or probabilistic) numerical weather prediction systems.
Contribute scientific understanding to inform how these systems are designed and how they can be leveraged to provide effective decision-support guidance to end users.
Contribute to the refinement of existing ensemble generation and evaluation methods.
Apply scientific understanding to help identify and implement innovations for improving probabilistic forecast skill and spread.
Contribute to designing, conducting, and evaluating the outputs of hypothesis-driven ensemble numerical weather prediction experiments.
Help contribute to the goal of developing high-resolution, regional and global, ensemble weather prediction systems that are skillful, reliable, physically realistic, and useful to a wide range of stakeholders.
Apply scientific understanding to inform the design of skillful and reliable high-resolution ensemble weather prediction and decision-support systems.
Assist with developing and testing novel ensemble methods, including but not limited to artificial intelligence applications.
Contribute to designing, conducting, and evaluating hypothesis-driven experiments to test innovations for improving probabilistic forecast skill and generating appropriate dispersion.
Prepare briefing materials and presentations for branch management, internal meetings, conferences, and workshops, and regularly publish scientific content.