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