26-118: Ensemble Prediction Scientist

Colorado State University; Boulder (CO); Hybrid; USD 115,000 - 125,000; Full time

Job summary

Colorado State University’s Cooperative Institute for Research in the Atmosphere (CIRA) seeks a full-time Research Scientist to lead development of next-generation ensemble numerical weather prediction systems in collaboration with NOAA GSL EPAD. The role focuses on scientific leadership, method development, and hypothesis-driven experiments to improve probabilistic forecasts.

The position reports to the CIRA Associate Director and supports high-resolution regional/global ensembles with

Qualifications

• Ph.D. in Atmospheric Science or related field.
• 7+ years developing, executing, and evaluating forecasts from numerical weather prediction systems.
• 3+ years experience in Fortran and/or C++, Python, HPC, and Linux computing environments.
• 3+ years formulating scientific priorities as it relates to numerical weather prediction.
• Experience communicating scientific results through conferences, seminars, technical reports or publications.
• U.S. citizens or permanent residents with a Green Card and eligible for background checks.

Responsibilities

• Serve as scientific lead for high‑resolution ensemble weather prediction system development.
• Lead implementation and refinement of current-generation ensemble prediction methods.
• Contribute to development of novel high‑resolution ensemble methods including mixed physics-based and data-driven ensembles.
• Design, conduct, and evaluate hypothesis-driven experiments to test innovations for improving probabilistic skill.
• Lead verification of ensemble systems using state-of-the-art methods and develop new ones.
• Prepare briefing materials and publish scientific content.

Skills

Fortran

C++

Python

HPC

Linux

Scientific communication

Education

Ph.D. in Atmospheric Science or related field

Tools

NOAA Unified Forecast System (UFS)

WRF model

MPAS

Job description

Job Description

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.

Full Consideration Date

Monday, August 17, 2026, 11:59pm (MT).

Essential Job Duties

• 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.

Conditions of Employment

• This position allows for a hybrid working modality that, with satisfactory performance, allows for up to 2 days per week of telework.
• This full‑time 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.

Supervision

The individual in this position will supervise 1-3 employees.

Required Qualifications

NOTE: In the body of your resume, please specifically address all required qualifications when describing your work experience. A resume that fails to specifically address the qualifications of this position may not be further considered by the search committee.

• Ph.D. in Atmospheric Science or related field, and
• 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.

Preferred Qualifications

NOTE: In the body of your resume, please specifically address all applicable preferred qualifications when describing your work experience. A resume that fails to address the qualifications of this position may not be further considered after review by the search committee.

• 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).

Required Application Documents

To apply, please upload the following applicant documents. Ensure your materials fully address the required and preferred job qualifications of the position. Please note, applicants may redact information from their application materials that identifies their age, date of birth, or dates of attendance at or graduation from an educational institution.

Cover Letter, Resume/CV

Salary Range

$115,000 - $125,000 commensurate with experience and qualifications.

Employee Benefits

Colorado State University is not just a workplace; it's a thriving community that's transforming lives and improving the human condition through world‑class teaching, research, and service. With a robust benefits package, collaborative atmosphere, and focus on work‑life balance, CSU is where you can thrive, grow, and make a lasting impact.

• Review our detailed benefits information here. (http://hr.colostate.edu/wp-content/uploads/sites/25/2021/01/benefits-summary-afap.pdf)
• Explore the additional perks of working at CSU here.
• For the total value of CSU benefits in addition to wages, use our compensation calculator. (http://hr.colostate.edu/total-compensation-calculator/)
• Lastly, click here for more information about why Fort Collins is consistently ranked in the top cities to live in!

Background Check Policy Statement

Colorado State University strives to provide a safe study, work, and living environment for its faculty, staff, volunteers and students. To support this environment and comply with applicable laws and regulations, CSU conducts background checks for the finalist before a final offer. The type of background check conducted varies by position and can include, but is not limited to, criminal history, sex offender registry, motor vehicle history, financial history, and/or education verification. Background checks will also be conducted when required by law or contract and when, in the discretion of the University, it is reasonable and prudent to do so.

EEO

Colorado State University (CSU) provides equal employment opportunities to all applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Inspired by its land‑grant heritage, Colorado State University is committed to excellence and engagement, setting the standard for public research universities in teaching, research, service and extension for the benefit of the citizens of Colorado, the United States, and the world.

Prospective Employees, click here to learn more about CSU's perks and purpose. (https://hr.colostate.edu/prospective-employees/)

Current employees will need to apply for jobs in Workday.