26-118: Ensemble Prediction Scientist

Colorado State University•Boulder, CO

1d•$115,000 - $125,000•Hybrid

About The Position

The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU) seeks to fill a full-time Research Scientist position designed to conduct collaborative research with the National Oceanic and Atmospheric Administration (NOAA) located at the Global Systems Laboratory (GSL) in Boulder, CO. The individual in this position will work in the NOAA/OAR/GSL Earth Prediction Advancement Division (EPAD) Physics Branch. CIRA at Colorado State University is a multi-million-dollar research organization located on CSU's Foothills Campus in Fort Collins, Colorado. CIRA is a cooperative institute that is also a research department within CSU's College of Engineering, in partnership with the Department of Atmospheric Science. Its vision is to conduct interdisciplinary research in the atmospheric sciences by entraining skills beyond the meteorological disciplines, exploiting advances in engineering and computer science, facilitating transitional activity between pure and applied research, leveraging both national and international resources and partnerships, and assisting the National Oceanic and Atmospheric Administration, CSU, the State of Colorado, and the Nation through the application of our research to areas of societal benefit. NOAA's Global Systems Laboratory is a federal science and research laboratory under NOAA’s Office of Oceanic and Atmospheric Research. GSL provides the National Weather Service (NWS) and the nation with environmental observation, prediction, computer, visualization, and information systems. These systems deliver data, forecasts, and predictions of weather, including severe weather events, within the next few minutes to weeks away. GSL is a leader in the applied research, directed development, and technology transfer of environmental data, models, products, and services that enhance environmental understanding with the outcome of supporting commerce, protecting life and property, and promoting a scientifically literate public. Position Summary: The individual in this position will lead the development of next-generation ensemble (or probabilistic) numerical weather prediction systems. They will serve as a scientific lead to inform how these systems are designed, how they can be leveraged to provide effective decision-support guidance to end users, and priorities for future developments. Major duties will include implementing and refining existing ensemble generation and evaluation methods; developing novel methods for generating and evaluating the outputs of ensemble numerical weather prediction forecasts; and designing, conducting, and evaluating the outputs of hypothesis-driven experiments to test innovations for improving probabilistic forecast skill and spread. The person in this role is expected to provide leadership in the Physics branch for the development of high-resolution, regional and global, ensemble weather prediction systems that are skillful, reliable, physically realistic, and useful to a wide range of stakeholders. This position will report to the CIRA Associate Director.

Requirements

Ph.D. in Atmospheric Science or related field
At least seven (7) years of experience in developing, executing, and/or verifying and evaluating the forecasts from numerical weather prediction systems.
At least three years of experience in Fortran and/or C++, Python, high-performance computing, and Linux computing environments.
At least three years of experience formulating and acting upon (and/or directing others in acting upon) scientific priorities as it relates to numerical weather prediction.
Experience communicating scientific results, such as through conference presentations, seminars, technical reports, and/or publications.
This position requires a National Agency Check with Inquiries (NACI), tier 1 federal background check and a NOAA Common Access Card (CAC) ID badge for systems access. Therefore, only US citizens and lawful permanent residents with a physical USCIS “Green Card” are eligible.

Nice To Haves

Knowledge of state-of-the-art ensemble weather prediction systems and their strengths and weaknesses as it relates to physical consistency and balancing probabilistic skill with appropriate forecast dispersion.
Knowledge of state-of-the-art methods for verifying ensemble/probabilistic weather prediction forecasts, including community and/or proprietary verification software containing these capabilities.
Knowledge of state-of-the-art regional (particularly thunderstorm-resolving) and/or global numerical weather prediction models.
Familiarity with data-driven (i.e., artificial-intelligence–based) weather prediction models, preferably those trained to provide probabilistic forecasts.
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

Serve as a scientific lead for high-resolution (convective-allowing) ensemble weather prediction system development to inform the design of future operational ensemble weather prediction and decision-support systems, including leading the formulation, execution, and tracking of priorities for ensemble prediction, verification, and visualization.
Lead the implementation and refinement of current-generation ensemble prediction methods within prototype high-resolution ensemble weather prediction systems.
Contribute to the development of novel high-resolution ensemble weather prediction methods, including but not limited to mixed physics-based and data-driven ensembles.
Design, conduct, and evaluate hypothesis-driven experiments to test innovations for improving probabilistic skill and generating appropriate dispersion within prototype high-resolution ensemble weather prediction systems.
Lead the verification of high-resolution ensemble weather prediction systems using state-of-the-art verification methods and potentially developing novel verification methods.
Prepare briefing materials and presentations for branch management, internal meetings, conferences, and workshops, and regularly publish scientific content.
Supervise 1-3 employees.