Seasonal-Forecast-System Data Assimilation Scientist

The fundamental goal of this work will be to advance the understanding and state of science for coupled assimilation components to be utilized for seasonal prediction and associated reanalysis.

 

Date posted

June 1, 2023 3:15 pm

Application deadline

July 31, 2023 5:00 pm

Organization

ESSIC/CICESS at the University of Maryland

Location

  • United States

Job description

Duties:The fundamental goal of this work will be to advance the understanding and state of science for coupled assimilation components to be utilized for seasonal prediction and associated reanalysis. The work will build upon efforts already underway for Unified Forecast System (UFS) based applications of Global Ensemble Forecast System Version 13 (GEFSv13) and Seasonal Forecast System (SFS), targeting a weakly coupled assimilation system from components that are currently under development. 

The candidate is expected to work on:

  • Development and implementation initialization methods to better blend and optimize increments for coupled model initialization.
  • Evaluation and assessment of the new method.
  • Investigating algorithmic updates, i.e., expanding Near Sea Surface Temperature (NSST) analysis from 3DVar to EnVar and Outer loops.
  • Initial exploration of improved background modeling and potential for the use of a novel Artificial Intelligence/Machine Learning (AI/ML) approach.

Qualifications: Knowledge, Skills, Abilities, and Background

Required:

  • Extensive knowledge of data assimilation techniques
  • Working knowledge of the physical and mathematical basis of geophysical modeling (atmospheric and/or environmental)
  • Basic knowledge of the United Forecast System and experience running advanced numerical weather prediction (NWP) models
  • Basic knowledge of Machine Learning / Artificial Intelligence algorithms
  • Understanding of predictability for earth system components
  • Ability and experience to work in High Performing Computing (HPC) environments for code development and algorithm implementation
  • Knowledge of programming languages such as object-oriented FORTRAN, Python, and/or C++ in a UNIX environment with advanced scripting languages
  • Ability to work in a group to achieve overall project goals, as well as the ability to conduct independent research to carry out assigned assignments
  • Good oral and written communication skills in English 

Desired:

Degree:

Terms: One year the first instance, with possibility of renewal 

  • Experience in running numerical models on HPC platforms using MPI, OpenMP, Slurm, LSF, etc.
  • Experience with coupled earth system models
  • Knowledge of 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.
  • Experience in model development in various infrastructures like the Earth System Modeling Framework (ESMF) and NOAA Environmental Modeling System (NEMS)
    • A Master’s degree (Ph.D. Strongly preferred) in Atmospheric and/or Oceanic Sciences, Applied Mathematics, Scientific Computing, Computational Science, or related field.

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 anegri@umd.edu.

THE UNIVERSITY OF MARYLAND IS AN EQUAL OPPORTUNITY AFFIRMATIVE ACTION EMPLOYER