Research scientist – CIRES

The Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado is seeking a research scientist with significant programming experience to work in the Forecast & Modeling Development Team of the NOAA Physical Sciences Laboratory (PSL). The candidate will work on the development of strongly coupled data assimilation methods that allow observations of the ocean/land (atmosphere) to impact the atmosphere (ocean/land) using ensemble-based estimates of error covariances. Part of this work will also focus on assimilation and bias correction of remotely-sensed soil moisture observations. The research and development will be done using the JCSDA Joint Effort for Data Assimilation Integration (JEDI) data assimilation framework and the NOAA Unified Forecast System (UFS).
The University of Colorado Boulder is committed to building a culturally diverse community of faculty, staff, and students dedicated to contributing to an inclusive campus environment. We are an Equal Opportunity employer, including veterans and individuals with disabilities.

Key Responsibilities:

  • Enhance the coupled data assimilation capability of the JEDI Local Ensemble Kalman Filter, by enabling the use of cross-component covariances to update the coupled UFS model states. 
  • Test the Local Ensemble Kalman Filter coupled data assimilation developed above in multi-year experiments with a prototype system assimilating a subset of the full global observing system to update the atmosphere and ocean states.
  • Complete additional experiments testing the benefit on weather forecasts of assimilating satellite soil moisture from the Soil Moisture Active Passive mission into the UFS.
  • Work with other PSL scientists to document and present the results in the form of journal articles, seminars, and conference presentations.
  • Work with scientists at PSL and the Environmental Modeling Center at the National Centers for Environmental Prediction (NCEP/EMC) to further develop the coupled data assimilation for use in operational global prediction.

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