Global sea Surface Temperature Scientist

ESSIC and the Cooperative Institute for Satellite Earth System Studies (CISESS) of the University of Maryland, College Park, are seeking a scientist to assist in the development of a new bias correction scheme for the 40+ year record of satellite global sea surface temperatures.

 

Date posted

Feb. 21, 2023 9:45 am

Application deadline

Mar. 21, 2023 5:00 pm

Organization

ESSIC/CICESS at the University of Maryland

Location

  • United States

Job description

ESSIC and the Cooperative Institute for Satellite Earth System Studies (CISESS) of the University of Maryland, College Park, are seeking a scientist to assist in the development of a new bias correction scheme for the 40+ year record of satellite global sea surface temperatures.  This work is a vital aspect of the production of NOAA’s next-generation long-term SST dataset, linking modern high-accuracy observations from current satellite data to those obtained from historical sensors.

Sea surface temperature (SST) is designated an Essential Climate Variable by WMO GCOS.  Since SST is representative of the directly accessible heat stored within the climate system, accurate knowledge of this parameter is key for successful prediction of both routine and extreme weather events, monitoring of regional and global climate change, prediction of ice formation and breakup, global heat transport, air-sea gas exchange, and monitoring of environmental conditions that may threaten ecosystems such as coral reefs.

The scientist will work as a NOAA/NESDIS/STAR team member on the development of a robust bias correction scheme for SST data derived from multiple new and historic sensors stretching back to the beginning of the satellite record.  This work, along with reprocessing of the satellite data being undertaken by other NOAA scientists, will form the basis of a new long-term record of global SST that will serve a wide range of users in climate monitoring & modeling, weather and ocean forecasting, cryospheric and environmental sciences.

The candidate will work primarily with experienced UMD and NOAA scientists on the development of new bias correction methodologies.  The work will require a combination of programming, “big data” analysis, understanding of physical processes and satellite retrieval methods.  There is also the possibility to explore the use of ML/AI techniques to uncover hidden patterns and increase understanding of processes that cause spatiotemporal biases in the various datasets.

The candidate is expected to contribute to the following:

  • Collection and analysis of various satellite SST datasets to characterize the regional and seasonal patterns of error
  • Comparison of different satellite sources to ascertain relative strengths and weaknesses
  • Obtaining and utilizing additional ancillary data to improve estimates of error, as determined by the above analyses
  • Understanding of the characteristics of current bias correction schemes in terms of strengths and weaknesses, including analysis to determine same
  • Development and validation of new methods for bias correction for the various sensors
  • Coding and running new bias correction schemes for the full ~40-year satellite SST dataset.
  • Iteration/refinement of the results, in concert with the satellite data reprocessing group tasked with making improvements to the satellite SST products.
  • Publishing research results in peer-reviewed journals and participate in conferences.

Qualifications:

Knowledge, Skills, and Abilities:

  • Strong Experience with code development in FORTRAN/C/Python, script language (bash, Perl, etc.) in Linux/Unix environments, and visualization coding in IDL, Matlab, and/or Python.
  • Experience processing large amounts of the observational dataset using multiple threads/clusters.
  • Experience with standard data format, e.g., netCDF-4, etc.
  • Basic knowledge of ocean-atmosphere physics related to SST and knowledge of mathematics equivalent to that needed for an undergraduate degree in natural science.
  • Ability to work in a group to develop work plans, and the ability to work independently to carry out assigned research assignments.

Desired Skills:

  • Machine Learning / Artificial Intelligence
  • Understanding of satellite retrieval methods and instrument calibration
  • Proven ability to work well in a team environment
  • Relevant peer-reviewed publications
  • Good writing and oral communication skills
  • Some ocean science experience desirable but not essential

Qualifications:

  • A Master's degree (Ph.D. preferred) in Atmospheric Sciences, Geophysics, Aerospace/Electric Engineering, Astronomy, Remote Sensing, Physics, or a related Physical Science.

Term: One year the first instance, with possibility of renewal 

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 aharris2@umd.edu.

THE UNIVERSITY OF MARYLAND IS AN EQUAL OPPORTUNITY AFFIRMATIVE ACTION EMPLOYER

 

For more details

https://essic.umd.edu/joom2/index.php/employment/3122-global-sea-surface-temperature-scientist