September 2012
Message 68

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[Met-jobs] WRF DART modeling postdoc position at Lawrence Livermore National Laboratory

From "Simpson, Matthew D." <>
To "" <>
Date Wed, 26 Sep 2012 17:07:55 -0700


A WRF DART modeling postdoc position is currently available at the Lawrence Livermore National Laboratory.


To apply:  go to and search for Job ID:  10755


NOTE:  This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years. For important information about LLNL appointments refer to the FAQs above. Eligible candidates are recent PhDs within five years of the month of the degree award at time of employment offer.


The Atmospheric Earth and Energy Division (AEED) within the Physical and Life Sciences (PLS) Directorate has an opening in the Energy Group to do original and independent research on development and application of ensemble-based wind forecasting methods. The researcher will work with the Weather Research & Forecasting (WRF) and Data Assimilation Research Testbed (DART) open source codes maintained by the National Center for Atmospheric Research (NCAR) and other forecasting systems.  This work is in support of research efforts at LLNL that seek to better understand error and statistical variation in wind and solar power forecasts, as this is needed to integrate increasing fractions of renewable generation into utility power.  Efforts look both at grid-scale planning needs and at individual plant performance forecast needs.  The postdoc will fill a key role in WRF-DART ensemble forecast methods, including input, execution and analysis of ensemble forecast runs using the LLNL high performance computing systems.  The selected candidate will be expected to take a leadership role in ensemble-based forecasting technology at LLNL, and will work collaboratively with NCAR on WRF-DART development topics. The position will report to the Energy Group Leader.



- Utilize publically available meteorological models (WRF and DART) to research the impact of initial condition and model error uncertainty on renewable energy (wind and solar) forecasts.

- Develop and validate new algorithms for quantifying renewable energy forecast uncertainty.

- Work with the LLNL researchers, research staff members, and personnel from other national laboratories, DOE and industry in collaborative research projects.

- Participate in program reviews, briefings to other organizations, conferences and other technical meetings.

- Publish both peer-reviewed publications and programmatic reports summarizing research findings.

- Present results at program meetings and national conferences.

- Perform all assignments in accordance with ES&H, security, and business practice requirements and policies.



- Recent PhD in atmospheric science, engineering, computational fluid dynamics or related field.

-  Experience running the Weather Research and Forecast (WRF) atmospheric model for research or operational forecast applications.

- Strong understanding of current data assimilation techniques, in particular ensemble based data assimilation.

- Experience estimating atmospheric forecast uncertainty using NCAR’s Data Assimilation Research Testbed (DART) software.

- Demonstrated ability to analyze and validate large model data sets. 

- Demonstrated written and verbal communication skills to work effectively in a diverse research team environment.

- Experience producing and presenting research results in programmatic and scientific meetings.

- Experience functioning as an independent researcher with a high level of attention to detail.

- Publication record in peer-reviewed literature with experience presenting research results to a large audience.



- Fluent in Unix based scripting languages (Python, Perl, etc.).

- Experience working with weather observations from the Meteorological Assimilation Data Ingest System (MADIS).

- Familiarity with statistical and graphical software packages for post processing WRF output (NCL, RIP, GrADS, Matlab, R, etc.).

Pre-Placement Medical Exam:  None required.

Pre-Employment Drug Test:  External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.

Anticipated Clearance Level:  None.






Matthew Simpson, Ph.D

Atmospheric Scientist

Lawrence Livermore National Laboratory


Tel: (925) 422-1034


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