WindBorne is seeking an exceptional meteorologist skilled in data assimilation and numerical weather prediction (NWP).
June 25, 2021 12:00 am
Aug. 25, 2021 12:00 am
Data Assimilation and NWP Meteorologist
WindBorne Systems is supercharging weather models with a unique proprietary data source: constellations of next-generation smart weather balloons targeting the most critical atmospheric data. Our long-term vision is to eliminate weather uncertainty, and in the process help humanity adapt to climate change, be that predicting hurricanes or speeding the adoption of renewables. The founding team of Stanford engineers was named Forbes 2019 30 under 30 and is backed by top investors including Khosla Ventures.
WindBorne is seeking an exceptional meteorologist skilled in data assimilation and numerical weather prediction (NWP) to assimilate our unique in-situ, atmospheric data source. As our most important meteorological hire, you’ll be building weather prediction with data no-one else has access to. You will be on the forefront of weather, growing a team to help humanity adapt to climate change.
Please email CEO Paige Brown at [email protected] with your resume if you’re someone who wants to create impact, not only in shaping a company from its early stages, but in the world at large in our fight to mitigate the risks climate change brings to the table.
- Assimilating data from targeted in situ atmospheric observations into numerical weather models
- Assessing and determining the best locations to target to maximize data impact
- Growing and leading an exceptional team of meteorologists
- Coordinating with our meteorology partners at NOAA and NCAR
- Shaping a vision for how WindBorne develops a forward-thinking NWP system that highlights the value of these unique observation sources
- Deep experience with WRF or similar numerical weather models
- Deep experience with a data assimilation system compatible with the WRF model, e.g. WRFDA, GSI, DART or JEDI
- End to end experience tuning model parameters and post processing
- Experience analyzing and evaluating model performance
- MS in meteorology or similar field
- Strong meteorological intuition
- Self-directed, creative, and scrappy
- PhD in meteorology or similar field
- Experience building an end to end real time forecasting system
- Strong leadership abilities
- Experience with cloud-based infrastructure, eg AWS, Google Cloud
- Experience with ensemble forecast sensitivity analysis
- Experience with ensemble and 4DVAR data assimilation
- Experience assimilating upper-air observations
- Familiarity with Linux
- Experience with MPAS
- Experience with FORTRAN
- Experience with Machine Learning