Atmospheric Data Scientist - 135288

The Center for Western Weather and Water Extremes (CW3E) is leading the development and implementation of new research and applications capabilities focused on extreme weather and climate events in the Western US. CW3E is developing and implementing weather and climate observations, numerical models and decision support tools focused on atmospheric and land-surface conditions related to extreme events such as heavy rainfall and their impacts. CW3E is an interface between UCSD departments, other universities around the country and local, state, and federal agencies, in the operation, utilization and expansion of a new state-of-the-art weather observing network recently deployed in California. Efforts to be undertaken by the CW3E Center include scientific research, technology development, numerical model development and testing, and development/deployment of smart tools that aid decision makers affected by extreme weather, water and climate events. A key activity of the CW3E Center is the development of methods and systems to aid future reservoir operations for flood control, water supply and ecosystems. CW3E is an international leader in atmospheric river research.

The Atmospheric Data Assimilation Scientist will contribute to the development, implementation, verification, and enhancement of Numerical Weather Prediction (NWP) models. This role focuses on advancing radar data assimilation techniques for CW3E's in-house models and evaluating the impact of reconnaissance observations-such as dropsondes and airborne radars-on both CW3E's models and cutting-edge community NWP systems.

Responsible for developing and refining forecast models and data assimilation schemes, with a focus on atmospheric rivers and their cascading impacts. Analyzes large datasets, including model outputs and multi-platform observations to assess forecast performance. Collaborates with CW3E colleagues and external partners on data assimilation systems, high-resolution simulations of landfalling atmospheric rivers, and extreme precipitation events in the western U.S. Communicates research findings through conference presentations, peer-reviewed manuscripts, technical reports, and other publications. Supports proposal development and project reporting while mentoring interns and early-career researchers.

• Bachelor's degree in Atmospheric Science or related fields related area and / or equivalent experience / training. Masters degree or Ph.D. is preferred.

• Experience running numerical weather prediction models on high-performance computing (HPC) platforms.

• Strong background in data assimilation, numerical modeling, and interdisciplinary collaboration to advance forecasting capabilities and improve the understanding of atmospheric river dynamics and predictability.

• Thorough knowledge of research function. Ability to work with large datasets with different formats and components, including NetCDF, GRIB, HDF, and binary.

• Thorough skills associated with statistical analysis and systems programming. Proven expertise in programming to include shell scripting, Fortran, C/C++, Python, NCL, and Matlab, amongst other programming languages. Must be proficient in Linux environments.

• Thorough skills in analysis and consultation. Strong time management skills. Ability to prioritize tasks appropriately and meet deadlines.

• Experience and proficiency in implementing and developing radar data assimilation in GSI/JEDI/WRFDA/DART. Experience in setting up, running, and analyzing output from numerical weather prediction models, such as WRF, MPAS, or similar.

• Skills to communicate complex information in a clear and concise manner both verbally and in writing. Skills in scientific visualization, scientific writing, and experience presenting at scientific conferences (poster and oral presentations).

• Excellent interpersonal skills, including thoughtfulness, diplomacy, and flexibility, with a strong emphasis on teamwork and recognizing the contributions of colleagues in a professional and supportive manner.

• Job offer is contingent upon satisfactory clearance based on Background Check results.

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