IMSG is seeking a candidate to support the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) in conducting research and development work related to the future operational marine component of the Unified Forecast System (UFS). Job ID 2022-1270
Apr. 11, 2022 14:00
Deadline for application
May 11, 2022 17:00
IM Systems Group, Inc.
IM Systems Group, Inc. (IMSG) is seeking a candidate to support the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) in conducting research and development work related to the future operational marine component of the Unified Forecast System (UFS). The candidate’s work will involve the development, implementation, testing and evaluation of various components of the marine data assimilation system within the framework of coupled assimilation and modeling. The place of work is the NOAA Center for Weather and Climate Prediction (NCWCP) in College Park, MD.
The candidate will perform the job functions in a collaborative, independent and high-quality manner, assisting in project management and developing and applying innovative methods for the main areas of work described below. The candidate will work with EMC scientists and external collaborators to develop marine and coupled data assimilation capabilities for use in the UFS. In particular, the developments will build on and contribute to the Joint Effort for Data assimilation Integration (JEDI) project. The main focus will be the JEDI marine project with contributions to coupled model development in support of data assimilation development activities.
The selected candidate will work on the following scientific and engineering tasks:
- Advance ocean DA development as part of marine DA development, as well as to assess the scientific quality of ocean reanalyses for climate and coupled sub-seasonal-to-seasonal (S2S) systems.
- Further development of ocean and coupled data assimilation capabilities within JEDI, focused on the marine environment.
- Investigate coupled model configuration and parameterizations at interfaces between interacting components of coupled earth system models.
- Support and further develop the UFS subsystems and their communication between the model and the data assimilation components.
- Evaluate the scientific quality of the ocean reanalysis produced by Hybrid-GODAS against observational datasets and other operational ocean reanalyses.
A master’s degree or higher in atmospheric sciences or related meteorology, oceanics, mathematics, or physical sciences with at least 2 years of experience in coupled forecasting and analysis systems.
Demonstrated knowledge, skills, and abilities in at least five of the following areas:
- Expert knowledge of ocean data assimilation with a focus on DA development and global applications.
- Advanced knowledge of the physical and mathematical foundations of geophysical modeling (ocean and atmospheric) and experience in running advanced numerical weather prediction (NWP) models.
- Experience in developing models in various infrastructures such as the Earth System Modeling Framework (ESMF) and the NOAA Environmental Modeling System (NEMS).
- Advanced knowledge and experience in modern programming languages such as object-oriented FORTRAN, Python and/or C++.
- Experience working in a UNIX environment with advanced scripting languages
- Experience working with HPC platforms (MPI, OpenMP, etc.).
- Modern software engineering practices (requirements gathering, design, prototyping, version control, integration, testing, and documentation).
- Experience in model testing and evaluation and/or knowledge of verification principles.
- Ability to work independently and in a team environment on complex problems.
- Good oral and written communication skills in English.
- Demonstrated ability to perform tasks that require organization and attention to detail.
Apply online at https://carreras-imsg.icims.com. In your cover letter, please provide the following information:
1) Availability schedule
2) Citizenship status
3) Salary requirements for consideration
NOTE: provide three references
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