Data Assimilation Scientist

Starting Salary: Commensurate with experience
Closing Date: Open until filled

Duties: ESSIC and the Cooperative Institute for Satellite Earth System Studies (CISESS) of the University of
Maryland, College Park is recurring a data assimilation scientist to support land surface data assimilation
research. Satellite land surface observations including but not limited to soil moisture, vegetation index, land
surface temperature, evapotranspiration and Snow Water Equivalent (SWE) data products have been
operationally produced and will be continue generated in the future. Given the Joint Effort for Data assimilation Integration (JEDI) was developed to unify community Data Assimilation (DA) system for research and operations, this project is proposed to improve the accuracy of Unified Forecast System (UFS) forecast through assimilating the primary satellite land surface data products.

Primary Duties:
 Test JEDI system in order to apply the satellite land surface observations;
 Conduct JEDI-based soil moisture DA, snow cover/SWE DA, and other land surface data assimilation;
 Implementation of a proper DA algorithm for Noah-MP land surface model of UFS within
preoperational/operational JEDI environment;
 Assimilation of the primary satellite land surface observations into the UFS via the JEDI system;
 Benefit analyses of data assimilation with respect to the surface air temperature and precipitation

 A Ph.D. in Earth science discipline (e.g., geoscience, atmospheric science), applied mathematics,
physics, scientific computing / computational science, computer science, or a related field.
 Working knowledge of programming languages such as Fortran and/or Python
 Ability to work independently and in a collaborative team environment
 Strong communication skills, both written and verbal in English
 An excellent record of disseminating scientific findings in peer-reviewed journals and/or professional

Preferred Qualifications:
 Familiarity with Linux system
 Familiarity with common data formats, such as NetCDF, HDF5
 Experience with land surface data assimilation is highly desirable
 Familiarity with NOAA’s operational data assimilation software (e.g., GSI)
 Familiarity with Numerical Weather Prediction (NWP) models
 Experience manipulating large volumes of data
 Experience running NWP models in a high-performance computing environment
 Experience plotting NWP weather forecast model output
 Experience evaluating NWP weather forecast model skills using statistical verification techniques
 Background knowledge of atmospheric physics and dynamic meteorology is an advantage
 Background knowledge of extreme events, such as drought and flood, is an advantage

To Apply: Interested candidates should send a CV with a list of at least 3 professional references and a cover
letter explaining how your qualifications meet the posted requirements to Dr. Jifu Yin at