Mesoscale Data Assimilation Scientist for GSD
The Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado and the NOAA Earth System Research Laboratory (ESRL) have an immediate opening for a research associate with experience in mesoscale satellite data assimilation and numerical weather prediction. The scientist will have an appointment within the Assimilation Development Branch (ADB) of the ESRL Global Systems Division.
The scientist will develop and test modifications in data assimilation procedures for satellite and other observations in mesoscale and storm-scale models, including assimilation of satellite-derived cloud and precipitation products. The scientist will work in collaboration with other scientists in ESRL/GSD/ADB. This work will include development for the HRRR and Rapid Refresh (RAP) assimilation systems using the GSI (Gridpoint Statistical Interpolation) assimilation and the WRF (Weather Research and Forecast) model. It will also involve work with NOAA’s Next-Generation Global Prediction System (NGGPS) including the FV3 model and the JEDI data assimilation system as it is developed with a focus on short-range forecast and nowcasting applications. The scientist will work as part of team in testing and refining algorithms for real-time operational application. The position is located in Boulder, Colorado. Salary will be commensurate with experience.
● M.S. in the atmospheric sciences or a closely related discipline
● Experience in developing and testing data assimilation techniques and codes
● Experience analyzing satellite observations and their impact in data assimilation systems
● Strong desire to improve operational NWP capabilities
● Strong desire to support research community in applying operational data assimilation systems
● Strong ability to communicate effectively (verbal and written) and work effectively in a team environment.
● Experience with numerical weather prediction models
● Experience with use of GSI data assimilation
● Experience with satellite observations including an understanding of the retrieval methods used to derive cloud and precipitation variables from satellite observations, and in particular the uncertainties in these retrieval methods
● Understanding of statistical principles underlying meteorological data assimilation
● Experience with ensemble-based data assimilation systems and their application
● Experience with WRF mesoscale model (use and development)
● Fluent knowledge of Fortran and Fortran 90 including debugging and optimizing code.
● Proficiency in UNIX-based scripting languages
● Expertise with Linux and UNIX operating systems
● Knowledge of synoptic meteorology and forecasting experience
● Experience maintaining robust community code in a real-time environment
● Experience with the verification of high-resolution meteorological forecasts
: Jul 12, 2018
To apply for this job please visit cu.taleo.net.