WRF Modeling Specialist

We're seeking a full-time, dedicated Weather Research & Forecasting Model (WRF) specialist whose primary focus will be supporting our precipitation enhancement programs. This position is exclusively focused on WRF modeling applications and optimization for modeling at  high spatial and temporal resolution.

• Model Configuration and Development: Configure and maintain WRF model setups in different domains. Simulate both convective processes in warm cloud systems, focusing on hygroscopic seeding to enhance precipitation through droplet growth and coalescence, and glaciogenic seeding in cold cloud systems, examining ice nucleation and crystal growth dynamics to evaluate precipitation enhancement and cloud microphysics. 
• Data Assimilation and Initialization: Design and implement data assimilation strategies using multiple data sources (surface observations, radiosondes, dropsondes, radar, satellite). Develop preprocessing workflows to ingest and quality-control observational data. Create automated procedures for model initialization and boundary condition updates.
• Model Analysis and Verification: Conduct comprehensive analysis of WRF model output focusing on cloud properties, precipitation processes, atmospheric stability, and QPE. Perform systematic model verification against observational data, and inform future measurement principles to improve model accuracy and confidence.
•  Precipitation Enhancement Applications: Validate and support precipitation enhancement operations by providing advanced forecasting models for convective storm systems at high spatial and temporal resolution.

• Master's degree or Ph.D. in meteorology, atmospheric science, remote sensing, or a related field.
• Demonstrated success in modeling convective storms system development, and its performance relative to ground truth. Strong understanding of atmospheric kinematics, electro and thermodynamics, and computation principles and meteorological processes.
• Experience with data acquisition, processing, and analysis of large datasets. This involves distributed computing methods, database management systems, data partitioning, vectorizing operations, and GPU acceleration.
• Experience with Amazon Web Services cloud computing for external data sources, and internal instance management.
• Proficiency in modeling, data analysis tools, and programming languages (e.g., Fortran, Python, MATLAB, R, C++).
• Knowledge of machine learning and deep learning techniques, for modeling applications.
• Previous experience working in both a research and operational environment.