Job Description Summary:UCAR is excited to announce the job opening for the CPAESS Project Scientist I role working with NOAA's Quantitative Observing Systems Assessment Program (QOSAP). This position will research to improve the use of radio occultation observations to improve global weather prediction. The incumbent will develop, implement, and test improved assimilation algorithms for radio occultation observations into NOAA's operational models, emphasizing improving assimilation algorithms, quality control procedures, and observation error characteristics. They will also be responsible for conducting numerical weather prediction experiments, interpreting the results, presenting findings at scientific meetings, and contributing to publishing results in scientific journals, as needed.

CPAESS serves the Earth System Science community in three distinct areas of service. We partner with federal agencies and businesses to hire critical scientific staff enabling a more robust workforce. Through our scientific programs, we seek to edify new research with our postdoctoral programs in collaboration with our visiting scientists, internship programs, and interagency support programs. We also help build the Earth system science community by planning summer schools, institutes, conferences, and advisory committees. Position Details: Visa Sponsored Job:No

Relocation Assistance Eligible:No

Job Location: Boulder, Colorado

Position Type & Term:Full time, Regular

Compensation Range: $92,103 - $115,128 Final salary and rates are based on education, experience, and skills relevant to the role. Application Notes Application Deadline:This position will be posted until Wednesday, May 22, 2024. Applications will not be accepted past this date.

Required application materials(preferably in PDF Format):

Resume

Cover Letter - Please address the following topics in your cover letter (for more information, please refer to the Key Responsibilities and Knowledge, Skills, and Abilities sections of this job posting):

Numerical Weather Forecasting Systems

Discuss your experience utilizing and working with numerical weather forecasting systems to generate accurate weather predictions.

Data Assimilation Systems

Describe your experience with data assimilation systems, how you ensure the accuracy and reliability of assimilated data, and your approach to integrating various data sources for weather prediction purposes.

Programming Languages

Outline your proficiency in programming languages you've used in the past as well as how you prioritize learning new programming languages or improving your skills in existing ones.

Federal Screening and Background checksare conducted for candidates selected for hire.

Work Location: This position is open to candidates seeking in-person, hybrid (combination of in-person and remote), and fully remote opportunities. Regardless of flexible work arrangements, UCAR requires ALL positions to be performed within the U.S., excluding U.S. Territories. What You Will Do Here is a summary of what one would expect to be generally responsible for in this role.

Key Responsibilities

Develop and test improvements to numerical algorithms, related to the assimilation of radio occultation observations in NOAA's models.

Conduct observing system experiments (OSEs) and observing system simulation experiments (OSSEs) to quantify and optimize the impact of radio occultation to improve numerical weather prediction.

Conduct numerical weather prediction experiments and interpret results.

Author and present scientific reports and publications and attend/give presentations at national or international scientific conferences.Who We'd Love To Join Our Team Education & Experience

A Ph.D. in atmospheric science or a related field with knowledge of numerical weather prediction, data assimilation,... For full info follow application link.

The Un versity Corporation for Atmospheric Research (UCAR) is an equal opportunity/equal access/affirmative action employer that strives to develop and maintain a diverse workforce. UCAR is committed to providing equal opportunity for all employees and applicants for employment and does not discriminate on the basis of race, age, creed, color, religion, national origin or ancestry, sex, gender, disability, veteran status, genetic information, sexual