Physical Scientist - Seasonal Forecast System Developer

Overview

Lynker is seeking a candidate to support the Office of Modeling and Development (OMD) of NWS in carrying out research and development work related forecast improvements for marine and seasonal forecasting. The candidate will be working on the physical model, Seasonal Forecast System (SFS) with a focus on the marine components, and development of OMD’s AI/ML prediction models for global coupled seasonal and marine forecasting. SFS is the Unified Forecast System’s (UFS) global-coupled seasonal forecasting application, which will replace CFSv2. The work location is at the NOAA Center for Weather and Climate Prediction (NCWCP) in Riverdale Park, MD.

 

The Ideal Physical Scientist and Seasonal Forecast System Developer will be able to work a hybrid schedule, working 2 days per week from the NOAA Center for Weather and Climate Prediction (NCWCP) office in College Park, MD, and the remaining 3 days per week from home.

Responsibilities

Duties of the Seasonal Forecast System Developer will include the following:

 

The candidate will perform job duties in a high-quality, independent, and collaborative way, developing and applying innovative methods for the primary work areas. The candidate will work with OMD scientists and external collaborators to develop and evaluate the current and future operational seasonal and marine forecasting systems. The candidate will work with the UFS infrastructure in designing and evaluating the global coupled ensemble forecasting systems for long range forecasting. Detailed responsibilities will include at least two aspects or more of the following areas.

• Development of global coupled Numerical Weather Prediction (NWP) models.
• Development of marine (ocean and sea ice) forecast systems.
• Development of subseasonal to seasonal probabilistic forecasting systems.
• Development of AI/ML techniques for seasonal and marine forecasting systems.
• Designing and conducting retrospective ensemble experiments and re-forecasts.
• Exploring initial or analysis uncertainties, and/or model uncertainties (or model stochastic perturbations).
• Analysis and evaluation of S2S probabilistic forecasts, and applications of multi-model ensembles and statistical post processing.

Qualifications

The Seasonal Forecast System Developer selected should have the following:

 

A Master or PhD’s Degree or equivalence in atmospheric sciences or meteorology, oceanic, mathematics, or related physical science with 1-2 years of experience in the area of global coupled ensemble forecast system development, sub-seasonal to seasonal prediction, numerical prediction, and/or AI/ML foundational prediction models. Candidates must be a U.S. Citizen or Permanent Resident Card Holder to qualify (please indicate in your cover letter).

 

Demonstrated knowledge, skills, and abilities in the following areas:

• Advanced knowledge of S2S prediction and experience in developing ensemble based and coupled SFS for research and operational implementation.
• Development of AI-ML methods for NWP and/or geophysical applications.
• Excellent knowledge and skills of modern programming languages and object-oriented languages, such as FORTRAN and Python.
• Skills and knowledge of the High Performance Computing (HPC) environment with strong scripting capabilities.
• Proficient with modern software development practices and version control software (e.g., Git and GitHub).
• Demonstrated skill in communicating effectively with scientists of diverse backgrounds on technical details of the work plan and present results accurately and clearly in both oral and written form.
• Ability to work independently on complex problems in a team and fast-paced environment and demonstrated skill in performing tasks requiring organization and attention to detail.
• Ability to support team initiatives, demonstrate respect for team members, and seek team consensus.

The Ideal Seasonal Forecast System Developer will have the following:

• Deep knowledge of ensemble strategy, reanalysis/reforecasting, and IC uncertainty.
• Familiarity with operational NWP and/or an operational forecasting environment and experience with advanced technical workflows.
• Experience with UFS applications and UFS infrastructure and knowledge of earth system modeling.
• Familiarity with agile software development practices.