Scientist Senior at SAIC

College Park, Maryland, United States -

Full Time

Start Date

Immediate

Expiry Date

27 Apr, 26

Salary

0.0

Posted On

27 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Assimilation, Aerosol Modeling, Computational Science, Python, FORTRAN, C++, UNIX, High-Performance Computing, JEDI Framework, Model Testing, Quality Control, Observation Diagnostics, Data Analysis, Numerical Prediction, Atmospheric Sciences, Meteorology

Industry

Defense and Space Manufacturing

Description

SAIC is seeking a highly-skilled and experienced candidate to support the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) in the cutting-edge research, development, and operational transition of the Global Chemistry Analysis and Forecast System (GCAFS) aerosol data assimilation (DA) within the Unified Forecast System (UFS) framework. The candidate's work will focus on the scientific advancement and successful implementation of the GCAFS aerosol DA system. The ideal candidate will be an expert in the Joint Effort for Data Assimilation Integration (JEDI) framework, advanced computational science, and sophisticated data assimilation principles to enhance the performance, stability, and future development of the GCAFS system. Job Description: The candidate will perform their job duties to a high standard, working both independently and collaboratively, focusing on scientific developments that advance GCAFS within the JEDI framework and ensure a smooth transition to operation. Lead the advancement and tuning of the JEDI-based GCAFS aerosol DA system Design, develop, and maintain advanced DA algorithms in the GCAFS system, focusing on key aerosol species Conduct comprehensive, long-term cycling data assimilation experiments Advance the use of observations and quality control in GCAFS. Improve data assimilation and observation diagnostics for GCAFS. Rigorously evaluate GCAFS aerosol analysis and forecast results against independent data, including CAMS, MERRA2 reanalysis. Investigate and resolve GCAFS model and DA issues, using data analysis tools to identify the cause of issues. Advance global workflow for GCAFS, ensuring readiness for both retrospective and real-time experiments. Assist in the operational transition of the GCAFS aerosol DA system Required Education: Bachelors and five (5) years or more experience; Masters and three (3) years or more experience; PhD and 0 years related experience Desired Education: A Master’s Degree or higher in atmospheric sciences, meteorology, physical oceanography, or related physical science with at least 8 years of experience in numerical prediction and data assimilation systems. Required Experience: Expert knowledge of the physical and mathematical basis of geophysical modeling. Advanced knowledge and hands-on experience with the Joint Effort for Data Assimilation Integration (JEDI) framework. Experience with Python and associated libraries used for data analysis in geosciences. Advanced knowledge and experience of modern programming languages such as object-oriented FORTRAN and/or C++. Experience in model testing and evaluation and/or knowledge of verification principles. Experience working in a UNIX environment with advanced scripting languages Experience with high-performance computing (HPC) environments and technologies such as MPI and OpenMP. Ability to work independently and in a team environment on complex problems. Good oral and written communication skills in English. Desired Experience: Knowledge of DA application to aerosols and atmospheric chemistry Knowledge of aerosol modeling, model physics and emissions tuning. Knowledge of Earth observing systems used for aerosols and chemistry. Knowledge of modern software engineering practices (version control, integration, testing, and documentation). Experience in running numerical models on High Performing Computer (HPC) platforms using MPI, OpenMP, Slurm, LSF, etc. Demonstrated skill in performing tasks requiring organization and attention to detail.

Responsibilities

The candidate will lead the advancement and tuning of the GCAFS aerosol data assimilation system and design, develop, and maintain advanced data assimilation algorithms. They will also conduct long-term cycling data assimilation experiments and ensure a smooth transition to operation.