Research Scientist for GNSS Radio Occultation and GNSS Reflectometry Data Assimilation
Date posted
Sep. 7, 2023 9:30 am
Application deadline
Oct. 31, 2023 5:00 pm
Organization
ESSIC/CICESS at the University of Maryland
Location
- United States
Job description
Duties:
ESSIC and the Cooperative Institute for Satellite Earth System Studies (CISESS) of the University of Maryland, College Park, are seeking an experienced data assimilation scientist to support Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) data assimilation research and development work at NOAA/NESDIS/STAR. GNSS RO and GNSS-R data assimilated in operational numerical weather prediction (NWP) models have been shown to reduce forecast temperature in the upper troposphere and lower stratosphere (UTLS) and wind errors. However, challenges remain in designing optimal GNSS RO and GNSS-R data assimilation algorithms. They include accurately characterizing these observations’ error characteristics and improving the forward operators used to simulate them from model fields. Further work is also needed to understand better the value obtained from assimilating GNSS RO and GNSS-R data from various government-sponsored and commercial providers for operational NWP forecast improvement. The qualified candidate will work with the NOAA STAR GNSS RO and GNSS-R teams to conduct the related data assimilation impact studies.
Primary Duties:
1. Evaluate data assimilation impacts: Design and run Observing System Experiments (OSEs) for assessing the impact of assimilating GNSS RO and GNSS-R observations in global NWP models.
2. Observation error estimation: Characterize the observation error matrix for GNSS RO and GNSS-R government-sponsored and commercial platforms under different atmospheric conditions. Starting with observation-minus-background (O-B) statistics generated from OSE experiments, explore using previously developed techniques and novel approaches to estimate various sources of observation and forward operator error.
3. Data assimilation algorithm improvement: In support of NOAA’s global NWP model improvement efforts, update or improve GNSS RO and GNSS-R data assimilation algorithms. This work could include improving forward operators or observation quality control schemes. Evaluate the impacts through OSE sensitivity studies.
4. Research and Innovation: Stay updated with the latest GNSS RO and GNSS-R data assimilation technique developments and related atmospheric science research and apply them when improving data assimilation algorithms.
5. Collaborative Projects: Work closely with other researchers in our team and other teams to support collaborative projects, contributing your expertise in GNSS RO/GNSS-R data assimilation and NWP model performance evaluation.
6. Technical Reporting and Publication: Prepare comprehensive technical reports and presentations to effectively communicate research findings and project progress to internal and external communities. Publish research results in peer-reviewed journals.
Qualifications:
- A Ph.D. in atmospheric science, data assimilation, numerical weather prediction, or related fields.
- At least three years’ work in fields related to this position’s duties (can include time spent on a Ph.D. research project)
- US citizenship or Permanent Resident (Green Card holder)
- Working knowledge of programming languages such as Fortran and scripting languages such as Python, GrADS, and IDL
- Ability to work independently and in a collaborative team environment
- Strong communication skills, both written and verbal
Preferred Qualifications:
- Familiarity with GNSS RO and/or GNSS-R observations and their data assimilation algorithms
- Experience working with NOAA’s operational data assimilation software (e.g., GSI or JEDI)
- Experience working with global NWP models such as the Global Forecast System (GFS)
- Experience running NWP models in a High-Performance Computing (HPC) environment
- Experience plotting NWP weather forecast model output
- Experience evaluating NWP weather forecast model skills using statistical verification techniques
- Background knowledge of atmospheric physics and dynamic meteorology
- Background knowledge and interest in extreme weather events that occur on scales large enough to be captured by global NWP models (e.g., tropical cyclones, atmospheric rivers, and synoptic winter storms)
To Apply: Interested candidates should send a CV with a list of at least 3 professional references and a cover letter explaining how your qualifications meet the posted requirements to xshao@umd.edu.
THE UNIVERSITY OF MARYLAND IS AN EQUAL OPPORTUNITY AFFIRMATIVE ACTION EMPLOYER
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