Job Listings

The Department of Geosciences at Texas Tech University (https://www.depts.ttu.edu/geosciences/) seeks an early-career postdoctoral scholar to provide scientific and meteorological expertise in the areas of lidar based remote sensing of boundary layer turbulence and thermodynamical processes over arid region. The scholar will collaborate on the newly developed ABL-ROAD (Atmospheric Boundary Layer Research Over Arid Domains) program. In addition to empirical boundary layer process studies, the postdoctoral researcher will also perform weather-scale analyses of model data mismatch (MDM) experiments using archived simulations from High Resolution Rapid Refresh (HRRR) models for the broader region.

As part of this opportunity, the researcher will explore and develop new observational tools and MDM approaches for boundary layer processes focusing on the impact of dryland ecosystem on boundary layer kinematics and thermodynamics. Furthermore, the researcher will explore the potential for new and emerging boundary layer profiling systems including lidar, sodar and ceilometer to enhance our knowledge on the boundary layer parameterization schemes. The key tasks involved in the project are to (1) acquire and analyze new lidar and in-situ measurements under appropriate synoptic conditions to document boundary layer turbulence processes over an arid region; (2) synthesize observations obtained from the West Texas Mesonet for time periods > 5 years; (3) systematically investigate how model errors vary as a function of soil-vegetation-atmosphere nexus and associated meteorological conditions; (4) provide insights into the soil moisture control on land-atmosphere interactions toward help developing new parameterizations; and (5) explore the coupling between soil moisture, precipitation, and near-surface temperature in transitional regions (e.g., semiarid and semi-humid) of southwest US.

The incumbent will work with Prof. Sandip Pal and his group members of Boundary Layer Meteorology research group (https://sites.google.com/view/sandippal-ttu/). The position will be based in Lubbock, TX, a mid-sized metropolitan located in the heart of West Texas.

Required Qualifications

Specific qualifications include:

• Completion (by the time of the appointment) of a Ph.D. in meteorology/atmospheric science/physics/environmental science or related field.
• A documented record of research experience in lidar operation, data acquisition and analyses, and interpretation.
• Experience in leading peer-reviewed publications

Preferred Qualifications

In addition to the required qualifications, individuals who have one or more of the following preferred qualifications are strongly encouraged to apply:

• Experience with field work and analyses of multivariable meteorological data sets obtained via ground-based meteorological stations.
• Knowledge, and expertise in areas of ground-based remote sensing, dynamics of the boundary layer, and land atmosphere feedback processes
• Experience in the evaluation of numerical model simulations

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The selected candidate will join the BNL’s Center for Multiscale Applied Sensing team primarily as part of the new Southwest Urban Corridor Integrated Field Laboratory team. The Southwest Urban Corridor Integrated Field Laboratory is a multi-institutional project supported by the Department of Energy’s Biological Environmental Research program. The project aims to integrate a diverse suite of high-resolution observations (atmospheric, land surface, and infrastructure), diagnostic/predictive models, and civic engagement to provide new knowledge and deliver next-generation predictive tools. These tools are meant to promote equitable policy interventions targeting extreme climate events, carbon dioxide emissions, and local air pollution within and across the Arizona urban corridor.

The postdoctoral researcher will engage in field work to collect unique weather observations using the CMAS mobile observatory trucks. These observations will then be analyzed by the postdoctoral researcher and the project team to (i) create new understanding of the feedbacks among urban infrastructure, waste heat, and extreme heat/weather events; (ii) assess the spatiotemporal patterns of extreme heat and air quality impacts across the diverse urban landscape; (iii) evaluate the efficacy and social equity of mitigative actions across communities that include traditionally underrepresented groups. This position has a high level of interaction with an international and multicultural scientific community.

Essential Duties and Responsibilities

• Design field deployment strategies to evaluate and inform next-generation predictive urban climate models (e.g., using OSSEs (Observing System Simulation Experiments) or ablation studies, through extensive literature review of past field campaigns, through interactions with project stakeholders)
• Participate in summer field work
• Conduct observationally based research into the urban boundary layer using “big data” (e.g., crowd sourced data or from mobile and distributed instrument networks)
• Assess the performance of next-generation predictive urban climate models using observations
• Develop new visualization strategies of observational data useful to scientists and stakeholders
• Write and publish results in peer-reviewed journals, in coordination with team leadership
• Report on results at regular group meetings and during scheduled seminars and colloquia
• Participate in conferences and workshops, regular group meetings, and assist with notetaking

Required Knowledge, Skills, and Abilities

• PhD degree in meteorology, atmospheric sciences, data science, mathematics, or closely related fields (e.g., physics)
• Demonstrated experience with data science, especially statistical hypothesis testing, uncertainty quantification, pattern analysis
• Excellent programming skills in various platforms and languages (e.g., Matlab, Python)
• Ability to work independently and collaboratively
• Clear and concise verbal and written communication and presentation skills
• Must have a valid driver’s license accepted for driving in New York State, or the ability to attain one within 1-month of the date of hire
• Ability to drive a light-duty (“non-CDL”) truck for long durations (up to 8 hours per day)

Preferred Knowledge, Skills, and Abilities

• Experience with weather instrumentation operation (lidar, radar, weather station, radiometer, sonde, cameras)
• Experience with weather instrumentation data analysis (lidar, radar, weather station, radiometer, sonde, cameras)
• Experience with mining and cleaning “big” observational data and/or online databases
• Experience with interfacing climate observations and numerical models (including the use of instrument forward simulators)
• Experience with developing multi-dataset or multi-dimensional visualization tools
• Experience with analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM)
• Experience with machine learning and artificial intelligence techniques
• Experience with predictive modeling

Review of applications begins in April 2025. Applications will be accepted until the position is filled

Research is under the direction of Dr. Katia Lamer

We are seeking two highly motivated postdoctoral researchers to support model system development and forecast applications for the DAWN project, an interdisciplinary project focused on making climate research accessible to support agricultural decision-makers in the United States. Thes roles will focus on operating and evaluating coupled regional Earth system models, including climate, hydrology, and agroecosystem processes. Additionally, the postdocs will work with an interdisciplinary group to support the transfer of the resulting forecasts to our existing online decision support infrastructure. Salary is commensurate with experience and University benefits will be included.

The key responsibilities of the postdocs will be to: conduct operational runs with a regional downscaling climate model, using data from operational NOAA seasonal forecasts; 2) conduct operational simulations for county-level crop forecasts using the DSSAT crop model; 3) run scenario analyses, perform sensitivity experiments, and optimize model performance; 4) evaluate model performance, predictive skill, and uncertainty; 5) monitor system and detect/solve abnormal conditions; 6) visualize results and package data for integration into the online decision support system; 7) communicate with interdisciplinary team about the data and results, including explaining to non-experts; 8) develop technical notes, user manuals, and peer-reviewed journal articles.

Applicants should have a recent Ph.D. (within 5 years) in agricultural, hydrologic, atmospheric or climate sciences, and a strong background in model development and application. They must have experience and skill in programming (particularly Fortran), as well as analytical abilities in model evaluation and climate-hydrology-crop interactions. Applicants should also have an interest and willingness to cooperate and communicate with an interdisciplinary team, and contribute to the develop of unique solutions to support moving research to operations.

This is a one-year appointment renewable depending on performance and funding availability.

To apply:

Interested applicants should submit a cover letter, CV, and contact information for three references to Professor Xin-Zhong Liang at xliang@umd.edu. This position is available immediately, and applications will be reviewed on a rolling basis until the position is filled.

KRQE News 13, is looking for a talented weekend morning Meteorologist/Producer. In this position, you will forecast weather conditions and anchor (3-hours) weekend weather reports in a manner that is clear, engaging and meaningful to our news consumers. In addition, the ideal candidate will produce weather graphics and produce a weather show, "Beyond the Forecast" featured on our CTV App during the week.

If you can tell a great weather story no matter what the screen is, then this position is for YOU!

• Produces and presents weather reports for all platforms
• Produce a 30-minute weekly program called "Beyond the Forecast"
• Ensures that all weather content meets company standards for journalistic integrity and production quality
• Writes and delivers weather stories in a clear and concise manner
• Assists in writing, copy editing, researching and coordinating weather programming and other content
• Responds to breaking and/or severe weather events and other urgent newsrooms situations as required
• Works closely with the weather team to develop comprehensive weather coverage
• Participates in promotional activities including public appearances
• Writes web stories detailing the local forecast daily
• Interacts with viewers and users on social media sites
• Performs special projects and other duties as assigned

Requirements & Skills:

• Meteorology degree, or a related field, or an equivalent combination of education and work-related experience
• Experience encouraged but not required
• Strong understanding of the field of meteorology and a proven track record of forecasting
• Excellent communication skills, both oral and written with the ability to ad lib when required
• Superior on-air presence
• Experience guiding, directing and motivating others
• Ability to identify the developmental needs of others and to coach, mentor or otherwise help others to improve their knowledge or skills
• Ability to meet deadlines, prioritize assignments and handle multiple tasks simultaneously
• Ability to effectively listen to fully understand problems and communicate with a team to shape a solution
• Valid driver’s license with a good driving record

Overview 

The Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) seeks to fill a Research Scientist position for its collaborative research as a Cooperative Institute with the National Oceanic and Atmospheric Administration (NOAA) Office, specifically with the National Severe Storms Laboratory (NSSL) in Norman Oklahoma. This Research Scientist position will contribute to the integration of Deep Learning within the Warn-on-Forecast (WoF) System (https://wof.nssl.noaa.gov/), housed at the National Weather Center in Norman, Oklahoma (https://www.ou.edu/nwc). A major activity for the WoF program is the development of a data-driven Deep Learning Artificial Intelligence (AI) based WoF system (WoFS) for next generation hazardous weather prediction. A successful candidate for this position will advance the data-driven WoFS, potentially revolutionizing how hazardous weather predictions are made.

Job Responsibilities

As a CIWRO Research Scientist supporting WoF program, you will:

• Lead the development of AI-based numerical weather prediction (NWP) emulation for WoFS, including open-source scientific Python packages for AI model training and forecast verification.
• Collaborate with research scientists to design and implement tools for WoFS data analysis, visualization, verification, and AI-driven advancements.
• Support the generation and archival of large-scale WoFS datasets to enable retrospective analysis and deep learning model training for future research.
• Serve as a lead or contributing author on scientific manuscripts for peer-reviewed publications and present research findings at conferences, workshops, and symposia.
• Creatively and efficiently solve scientific problems, both independently and as an integral member of the WoF research team.

Required Qualifications

• Ph.D. in Meteorology, Computer Science, Software Engineering or a related field.
• Solid understanding of convective weather phenomena, atmospheric models (both NWP and AI-based), and large meteorological datasets is crucial.
• Deep knowledge and experience in relevant sciences and technologies, including AI/deep learning, meteorology, programming, computing, and data science.
• Proficiency in languages like Python and experience with HPC environments.
• Excellent oral and written communication skills with an ability to work both independently and cooperatively with others.

Benefits and Work-Life Balance

Joining our team comes with numerous benefits, including:

• Competitive salary based on experience; comprehensive university benefits (http://hr.ou.edu/).
• Generous paid leave, encompassing 14 paid holidays and 22 hours of accrued paid time off per month.
• Reduced membership at the University of Oklahoma’s state-of-the-art fitness and aquatic center (https://www.ou.edu/far).

More details about working at the University of Oklahoma, benefits packages, as well as living in Norman, Oklahoma are provided on our website: https://jobs.ou.edu/Discover-OU.
We are dedicated to promoting a healthy work-life balance by championing a flexible work culture, offering adaptable work hours and a hybrid work arrangement. This empowering framework enables team members to seamlessly navigate personal commitments while effectively contributing to their professional responsibilities.

How to Apply

Applications should be mailed to ciwro-careers@ou.edu Attn: WoF AI/ML and include a cover letter, the names and contact information for 3 references, and your resume/cv. The cover letter must highlight your relevant qualifications and how they can contribute to the WoFS team. Applications will be accepted until the position is filled. The starting date is negotiable.
The University of Oklahoma is an equal opportunity/Affirmative Action employer.

Position Description

This position is for a postdoctoral scholar to work at the University of California, Davis, starting in the late summer/early fall, under the supervision of Prof. Adele Igel and in collaboration with project partners at the National Center for Atmospheric Research, the University of California, Berkeley, and the Stockholm Environment Institute on a project designed to evaluate and to understand how to optimize orographic cloud seeding in California to enhance precipitation and runoff into hydroelectric reservoirs by examining the lifecycle of enhanced snowfall from seeding using a multidisciplinary approach. The successful candidate will be responsible for leading case study sensitivity tests to seeding operations and for running and analyzing seasonal ensemble simulations in collaboration with project partners. It is expected that the successful candidate will aid in preparation of progress reports to the funding agency and will prepare their results for peer-reviewed publication. The successful candidate will also be expected to aid in the supervision of more junior team members at UC Davis and will have the opportunity to interact regularly with a variety of more senior team members at the collaborating institutions.

Qualifications

Basic qualifications (required at time of application)
PhD in Atmospheric Science, Physics, or a related field will be required at the time of the start program in late summer, early fall.

Preferred qualifications (other preferred, but not required, qualifications for the position)

• Cloud physics and/or cloud seeding research experience
• Experience running WRF or a similar model
• Demonstrated ability to work in a team environment
• Potential to be a leader in developing scientific ideas
• Proficiency in coding in Python or a related application
• Proficiency in written and spoken English