Overview

Lynker is seeking two talented physical scientists to support development and operational transition of NOAA’s Warn-on-Forecast Systems (WoFS) under the Precipitation Prediction Grand Challenge (PPGC) program at the National Weather Service Office of Modeling and Development. The successful candidates will support the operational transition of the cloud-based WoFS version 1 which is built on the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) data assimilation system, ensuring  the system meets operational implementation requirements, expanding the system to provide domains beyond the CONUS region, while improving forecast of extreme precipitation events and other high impact weather such as  tornadoes. The candidates will also collaborate with the National Severe Storm Laboratory (NSSL) and other partners on the development of next generation of WoFS (i.e., version 2) based on the Model for Prediction Across Scales (MPAS) and the Joint Effort for Data Assimilation Integration (JEDI) system.

Responsibilities

Successful candidates for this position will support the operational transition of WoFSv1 and the development of WoFS v2 within a cloud environment, focusing on model development, system integration, testing and optimization, and transition of WoFS v1 capabilities into NWS operations.

 

Duties of the Physical Scientist and Warn-on-Forecast System Developer will include the following:

 

• Harden the cloud-based WoFSv1 system to meet the NWS NCO operational implementation requirements and support its operational transition.
• Conduct rigorous model performance evaluations, benchmarking (WoFSv1 vs. WoFSv2), as well as retrospective and real-time simulations to evaluate forecast skills, operational readiness, and overall system value.
• Expand the system’s geographical scope by developing the capability to run WoFS outside the CONUS domain.
• Develop new WoFS products and metrics for a wider range of high-impact weather hazards beyond severe convection, leveraging forecasters input.
• Support the development and testing of the MPAS-JEDI-based WoFS v2 prototype.
• Develop a robust, unified end-to-end workflow for the WoFSv2 system, integrating all components: model input preprocessing, data assimilation, forecasting, post-processing, product generation, and visualization.
• Establish, maintain, and manage GitHub repositories to support version control and code development.

 

 

Qualifications

The Ideal Physical Scientist and Warn-on-Forecast System Developer will have the following:

 

• Education: M.S or higher in Atmospheric Science, Meteorology, Computer Science, Software Engineering, or a closely related field.
• Advanced knowledge of Numerical Weather Prediction (NWP) modeling of convective and mesoscale meteorology, ensemble data assimilation, and atmospheric physics and dynamics.
• Experience in developing weather forecasting systems built on WRF and MPAS
• Experience in software development, code management (e.g., GitHub), and integrating complex modeling and data assimilation components.
• Experience assimilating weather radar, satellite, conventional, mesonet and conventional observations with GSI or JEDI to improve initial conditions for rapid-fresh, convection-allowing ensemble short-range weather prediction systems is preferred.
• Programming Skills: Strong proficiency in Fortran and modern scripting languages, particularly Python (including scientific packages for AI/ML training and verification). 
• Experience working within cloud High Performance Computing environments is highly desired.
• Communication: Excellent oral and written communication skills with a proven ability to work both independently and within a multi-disciplinary team.