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CIRES/NOAA PSL Postdoctoral Associate on Characterizing the Impact of Model Error

Employer
University of Colorado Boulder
Location
Boulder

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Position Type
Postdoc
Employment Type
Full Time
Institution Type
Four-Year Institution
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CIRES/NOAA PSL Postdoctoral Associate on Characterizing the Impact of Model Error and Bias on Subseasonal-to-Seasonal Forecast Skill Using a Hybrid Numerical Model-Machine Learning Approach

Requisition Number: 42806
Location: Boulder, Colorado
Employment Type: Research Faculty
Schedule: Full Time
Posting Close Date:
Date Posted: 14-Sep-2022

Job Summary
The issue of improving subseasonal-to-seasonal forecast skill inherently relies on enhancing our understanding of how model bias and forecast error associated with the simulation of specific dynamical modes of climate degrade forecasts. The University of Colorado's Cooperative Institute for Research in Environmental Sciences (CIRES) and the Physics Sciences Laboratory (PSL) at the NOAA Earth System Research Lab in scenic Boulder, Colorado, seeks an enthusiastic and capable postdoc to help us better understand these interactions in the context of temperature and precipitation forecasts over North America on subseasonal timescales. The hire will work on the development of several machine learning-based dynamical climate mode filtering techniques that will be subsequently integrated into the existing NOAA Physical Science Laboratory (PSL) Unified Forecast System (UFS) “nudging” framework, to determine the relative contribution of ENSO, the MJO, and various stratospheric processes to model errors and biases. The candidate will use additional empirical modeling techniques (e.g., Markov models) trained on UFS model output in order to determine which dynamical modes contribute most to forecast lead-dependent tropical errors and downstream extratropical errors, aiming to develop UFS model diagnostics and/or post-processing techniques for forecast improvement.

The candidate should have excellent technical and communication skills and be comfortable working in a team environment. This is a two-year position with the possibility of extension to a third year based on performance and achievement of project goals and the availability of funding.

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
Cooperative Institute for Research in Environmental Sciences

The Cooperative Institute for Research in Environmental Sciences (CIRES) is an internationally recognized leader in innovative environmental science and research and is located at the University of Colorado Boulder. At CIRES, the Cooperative Institute for Research in Environmental Sciences, 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 the University of Colorado 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 scientific and societal understanding of the Earth system.

Physical Sciences Laboratory

Our mission is to conduct scientific research to observe, understand, model, predict and forecast weather, water and climate extremes and their impacts. An informed society that uses science-based environmental intelligence to effectively anticipate and respond to threats and opportunities related to weather, water and climate extremes. Our research goals are as follows:
  • Rigorously characterize and predict weather, water, and climate extremes and their uncertainties to support NOAA's mission.
  • Develop new process understanding, observing, and modeling capabilities to predict conditions associated with too much or too little water for early warning, preparedness, resource management, and adaptation.
  • Improve monitoring and prediction of weather, climate, and water conditions impacting marine resources.


  • What Your Key Responsibilities Will Be

    1. Conduct research on subseasonal-to-seasonal forecasting and predictability that is relevant to the missions of NOAA PSL and NOAA CPC and their partners.
    2. Develop various machine learning and empirical models and run the NOAA-PSL UFS nudging platform
    3. Work collaboratively with researchers from a variety of earth science disciplines.
    4. Effectively share research results with colleagues and the broader science community through white papers, peer-reviewed publications, and presentations.


    What You Should Know

    • This position will be rostered in CIRES at the University of Colorado Boulder 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.
    • This is a two-year position with the possibility of extension to a third year based on performance and achievement of project goals and the availability of funding.
    • All University of Colorado Boulder employees are required to comply with the campus COVID-19 vaccine requirement. New employees must provide proof of vaccination or receive a medical or religious exemption within 30 days of employment.


    What We Can Offer

    • CIRES can offer a generous compensation package. Salary is commensurate with experience and determined based on our CIRES internal career track classification.
    • The annual hiring salary range for this position is $63,000 - $68,000.
    • We have a great work culture that emphasizes work-life balance.


    Benefits 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.

    Be Statements Be ambitious. Be groundbreaking. Be Boulder.

    What We Require

    • A Ph.D. in a physical science with expertise in meteorology, climate, applied mathematics, physics, or related discipline.
    • A strong publication and presentation record that is commensurate with experience.


    What You Will Need

    • Excellence in oral and written communication.
    • Excellence in self-guided, motivated, and original research.
    • Strong interpersonal skills to facilitate team building and knowledge sharing.
    • Desire and ability to contribute to an inclusive and collaborative work environment.




    Special Instructions
    To apply, please submit the following materials:

    1. Resume or CV.
    2. Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position.
    3. Submit a list of 3 professional references (the names, titles, affiliations, professional relationships, emails and phone numbers) who may be contacted to provide a reference or a letter of recommendation on your behalf if you are identified as a finalist for this role.

    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 (if applicable) as an optional attachment.

    Applications will be reviewed as they are received. This 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.


    To apply, visit https://jobs.colorado.edu/jobs/JobDetail/CIRES-NOAA-PSL-Postdoctoral-Associate-on-Characterizing-the-Impact-of-Model-Error-and-Bias-on-Subseasonal-to-Seasonal-Forecast-Skill-Using-a-Hybrid-Numerical-Model-Machine-Learning-Approach/42806







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