Machine Learning and Atmospheric Data Assimilation - 117410
#117410 Machine Learning and Atmospheric Data AssimilationExtended Review Date: Fri 9/23/2022
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This position will remain open until filled.
CW3E targets observations and forecasting of atmospheric rivers to improve real-time knowledge and response to water resource management challenges. We create new tools for weather prediction (through machine learning and data assimilation) to push the envelope in the use of unique observational data for forecast improvement. As part of the science team, this role will collaborate with other atmospheric modeling scientists and engineers to create a state of the art forecasting system that - through ensemble data assimilation and new computing platforms - produces game changing advances in predicting extreme events on the US west coast.
- Collaborate with an interdisciplinary team to create algorithms that leverage CW3E’s innovative observational capabilities.
- Bring unique expertise in machine learning and ensemble data assimilation to complement our numerical modeling team.
- Develop deep-learning methods to analyze and quality control inputs and outputs of the forecast system.
- Implement enhancements to next generation object oriented multi-model interfaces to incorporate state-of-the-art satellite observations.
The successful candidate will utilize strong programming skills and experience in machine learning, computational, and numerical methods to enhance data assimilation capabilities for numerical weather prediction models at CW3E. They will implement methods for simulating remote sensing observations from 4D numerical model fields and design forecast experiments using new remote sensing data. The project will use the Joint Effort for Data Assimilation Integration (JEDI) system, which is a new object oriented platform that interfaces numerical models (i.e. Model for Prediction Across Scales (MPAS), the Global Forecast System Finite Volume 3, and /or the Community Earth System Model), with any observational dataset, and includes a range of mathematical solvers to provide a high degree of flexibility for numerical modeling experiments for optimizing forecasts of atmospheric rivers.
Thorough knowledge of research function. Uses machine learning and atmospheric data assimilation to evaluate and analyze large datasets to improve model performance, for deterministic or probabilistic predictions.
Working knowledge of Atmospheric and/or Earth system modeling.
Experience with large scale computational implementations for scientific applications.
Experience with object oriented computing, workflow software development utilizing Github, Python, xarray, pandas and other code libraries for machine learning and large data applications.
Research skills at a level to evaluate alternate solutions and develop recommendations. This includes proposing new ML techniques, analyses, use of new datasets, new conceptual ideas, or new workflow recommendations.
Thorough skills associated with statistical analysis and systems programming. Experience with ensemble data assimilation and Kalman filtering techniques.
Skills to communicate complex information in a clear and concise manner both verbally and in writing. Skills in visualization and graphical representation. Experienced in preparation of scientific articles, posters, and presentations at conferences.
Skills in project management. Ability to work independently and within a team framework. Can prioritize tasks. Meets deadlines
Ability to supervise, train, delegate work to and or collaborate with junior scientists with diverse backgrounds.
- US Citizenship or Green Card is required.
Job offer is contingent on successful engagement in the UC COVID-19 Vaccination program (fully vaccinated with documented proof or approved exception/deferral).
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