Postdoctoral Fellow Theoretical and Applied Data Assimilation Scientist
The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University seeks to fill a postdoctoral fellowship as part of a National Science Foundation (NSF) award to be located at CIRA in Fort Collins, Colorado. This fellowship may last up to 3 years contingent upon NSF funding availability. The individual in this position will be part of the data assimilation group and will work on developing links between non-Gaussian distributions and different atmospheric scale dynamics, converting the hybrid version of WRF-GSI to have a non-Gaussian component, as well as developing and implementing different probability density functions detection algorithms.
Essential Job Duties:
Non-Gaussian Dynamical Linkages 40%
- develop mathematical and/or stochastical model to link probability density functions to different atmospheric dynamics;
- implement and test the new model with toy problems;
- implement and test the new model with the WRF test cases.
Non-Gaussian Hybrid WRF-GSI 40%
- convert the Weather, Research, and Forecasting Gridpoint Statistical Interpolation (GSI) and the hybrid system to allow for a lognormal component (e.g., for the moisture variable) as well as a version with the logarithmic transform approach;
- test and compare the different versions of the WRF-GSI against different distributed error scenarios;
- conduct WRF-GSI experiments to assess the impacts on short and medium range forecasts from the different configurations of the hybrid WRF-GSI system.
Dissemination of Research Results 20%
- prepare manuscripts for submission to peer review journals and edit through the review process;
- prepare and present conference abstracts, posters, and/or presentations;
- travel to domestic and international conferences.
- PhD by the start date in Physics, Mathematics, Statistics, Remote Sensing, Meteorology, or related physical science field plus 1 year of experience working with data assimilation systems;
- higher education in fundamental physics and/or mathematics;
- experience programming in Fortran90 or higher and Linux scripting;
- ability to travel to domestic and international conferences.
- experience working with variational data assimilation;
- knowledge of WRF-GSI;
- knowledge of Bayesian Theory;
- knowledge of the mathematical field of Numerical Analysis, i.e. preconditioning, numerical linear algebra, NSDE etc;
- knowledge of mesoscale and/or synoptic meteorology;
- stochastical modeling experience;
- machine learning experience;
- proficiency in MATLAB or equivalent analysis and display software.
Annual Salary: $51,000
Colorado State University (CSU) strives to provide a safe study, work, and living environment for its faculty, staff, volunteers and students. To support this environment and comply with applicable laws and regulations, CSU conducts background checks. The type of background check conducted varies by position and can include, but is not limited to, criminal (felony and misdemeanor) history, sex offender registry, motor vehicle history, financial history, and/or education verification. Background checks will be conducted when required by law or contract and when, in the discretion of the university, it is reasonable and prudent to do so.
Commitment to Diversity and Inclusion:
Reflecting departmental and institutional values, candidates are expected to have the ability to advance the Department's commitment to diversity and inclusion.
Application Deadline and How to Apply:
Applications will be accepted until the fellowship is filled; however, to ensure full consideration applications should be submitted by 11:59 PM MT on October 18, 2017. References may be contacted immediately and without further notification to the candidate. Apply electronically by clicking “Apply to this Job” at the following website: http://jobs.colostate.edu/postings/50134. NOTE: In your cover letter, please specifically address the required and preferred qualifications of this position. While it is not anticipated that any given applicant will satisfy 100% of the preferred qualifications, presenting your relevant experience/expertise via clearly articulated examples will be of great benefit to the search committee. A cover letter that fails to address the required and preferred qualifications of this position will result in immediate disqualification for the position.