CIRES/NOAA-GSL Boundary Layer Parameterization Post-Doctoral Researcher
The Cooperative Institute for Research in Environmental Sciences (CIRES) at CU Boulder encourages applications for a Post-Doctoral Researcher! This position will entail using observational data from the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) program to evaluate and help improve the representation of physical processes in NOAA weather/earth-system forecast models. The position is part of the collaborative research between CIRES and the National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research (OAR) Global Systems Laboratory (GSL) in Boulder, CO. The incumbent will work within GSL’s Earth Prediction Advancement Division (EPAD).
GSL has an extensive history building operational regional weather forecast models, such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models that are run operationally by the National Weather Service (NWS). NOAA is moving to a Unified Forecast System (UFS), which will use the same weather prediction modeling system for both global and convective allowing scales. The UFS development effort requires extensive model parameterization development and validation effort, to ensure that these parameterizations work at all scales.
DOE/ARM collects comprehensive observations from a network of six ground-based observatories and an aerial facility that provides supporting measurements to these ground-based observations. Data from these observatories have been important for the development and improvement of physical parameterizations of radiation, clouds, and aerosols in a number of models. To accelerate the application of ARM observations towards the improvement of NOAA’s UFS, this post-doctoral position will directly use the rich set of ARM observations to provide insights into forecast-error attribution by linking physical states in model forecasts to systematic errors. Some examples of the type of investigative research this post-doc is expected to perform can be found in Neggers and Siebesma (2013), Ahlgrimm and Forbes (2012), and Ahlgrimm et al (2016).
Research examples cited above:
Ahlgrimm, M. and R. Forbes, 2012: The impact of low clouds on surface shortwave radiation in the ECMWF model. Mon. Wea. Rev., 140, 3783–3794, https://doi.org/10.1175/MWR-D-11-00316.1
Ahlgrimm, M., R.M. Forbes, J.-J. Morcrette, and R.A.J. Neggers, 2016: ARM’s impact on numerical weather prediction at ECMWF. The Atmospheric Radiation Measurement Program: The First 20 Years, Meteor. Monograph, 57, Amer. Meteor. Soc., 28.1-28.13, doi:10.1175/AMSMONOGRAPHS-D-15-0032.1
Neggers, R.A. and A.P. Siebesma, 2013: Constraining a system of interacting parameterizations through multiple-parameter evaluation: Tracing a compensating error between cloud vertical structure and cloud overlap. J. Climate, 26, 6698–6715, https://doi.org/10.1175/JCLI-D-12-00779.1
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.
Who We Are
At CIRES, more than 800 environmental scientists work to understand the dynamic Earth system, including people’s relationship with the planet. CIRES is a partnership of NOAA and CU Boulder, and our areas of expertise include weather and climate, changes at the Earth’s poles, air quality and atmospheric chemistry, water resources, and solid Earth sciences. Our vision is to be instrumental in ensuring a sustainable future environment by advancing the scientific and societal understanding of the Earth system.
Global Systems Laboratory website:
What Your Key Responsibilities Will Be
- Use ARM observations to identify/quantify biases in boundary layer, cloud, radiation, and land surface properties and evolution that are predicted by the UFS model
- Analyze these biases to attribute them to specific processes that the model is trying to represent, and hypothesize improvements to the model parameterizations in collaboration with GSL scientists
- Implement these hypothesized improvements into the UFS parameterization schemes, and test them by running the modeling system on case studies
What You Should Know
- This position is available for a maximum of two years, contingent upon satisfactory progress in year one.
- The position will be rostered in CIRES, but will be physically situated in the David Skaggs Research Center, 325 Broadway, Boulder, CO 80305. If you are the selected finalist you will be required to pass a federal laboratory background clearance for site access.
What We Can Offer
We can offer a competitive salary and a comprehensive benefits package.
The University of Colorado offers excellent benefits , including medical, dental, retirement, paid time off, tuition benefit and ECO Pass. The University of Colorado Boulder is one of the largest employers in Boulder County and offers an inspiring higher education environment. Learn more about the University of Colorado Boulder .
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What We Require
- Ph.D. in atmospheric science (granted in the last 2 years or will be granted before the end of 2020) or a closely related field, such as physics or mathematics
- Hands-on experience (minimum of 1 year) running/developing numerical weather prediction models, such as WRF or FV3, preferably with knowledge of physical parameterizations and numerical methods.
- Proficient knowledge and experience with Fortran 90+ including debugging and optimizing code
- Proficiency in UNIX-based scripting languages, such as tcsh, bash, ksh, Python, or XML
What You Will Need
Ability to work and communicate efficiently (verbal and written) within a team environment and to facilitate communication across multiple teams and organizational units.
What We Would Like You To Have
- Excellent communication skills and experience in writing scientific publications.
- A strong background in synoptic/mesoscale/boundary-layer meteorology
- Experience in boundary-layer parameterization development and testing.
- Experience with model-observation comparisons and effective application of verification techniques.
- Knowledge of data analysis and statistics.
- Experience with handling datasets in netCDF and GRIB2 formats
- Experience with Linux/UNIX operating systems.
- Experience maintaining code using version-control software, such as GIT.
- Experience with high-performance computing (HPC) and parallel computing.
To apply, please submit the following materials:
- Resume or CV
- Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position.
- List of contact information for 3 references who will be willing to write a confidential Letter of Recommendation for you.
If you are selected as the finalist, your degree will be verified by the CU Boulder Campus Human Resources department using an approved online vendor. If your degree was obtained outside of the United States, please submit a translated version as an optional attachment.
Applications will be reviewed as they are received. Position will remain posted until filled
Note: Application materials will not be accepted via email. For consideration, applications must be submitted through CU Boulder Jobs .
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