The University of Maryland Cooperative Institute for Satellite Earth System Studies (CISESS) is looking for a postdoc to apply data assimilation methods to incorporate an existing global leaf area index (LAI) product into NOAA’s Unified Forecast System (UFS) and document the impacts to forecasted near-surface atmospheric temperature and humidity, and surface energy and hydrologic budgets. A phenology-informed LAI seasonality framework will be developed to improve representation of green-up, peak canopy, and senescence in UFS applications. The candidate will work closely with scientists at the National Weather Service (NWS) Office of Modeling and Development (OMD) and CISESS to conduct the research. This position is initially for one year, with the possibility of renewal contingent upon performance and funding availability.
The applicant will be expected to:
- Incorporate existing LAI observations into the UFS land model to simulate surface radiation partitioning, precipitation interception, and transpiration across all global ecosystems.
- Update existing physical parameterizations to more effectively use LAI information to affect plant processes such as photosynthesis and canopy radiation transfer.
- Develop or refine existing data assimilation algorithms to initialize phenology models with satellite-based LAI to improve forecasted surface vegetation, including dynamically updating phenology model parameters at initialization.
- Work within a hierarchical development environment that involves a spectrum from land-only simulations to fully-coupled Earth System Models.
- Identify and organize data specific to surface processes (turbulent fluxes, albedo, soil moisture) for model parameterization development, testing and evaluation.
- Prepare peer-reviewed manuscripts and technical reports based on data assimilation and modeling results.
Qualifications:
Ph.D. within three years of graduation in Atmospheric Sciences, Hydrology, or a related environmental science field with a strong emphasis on applied mathematics, numerical methods, and modeling is required.
Candidates must have:
- A research background in atmospheric science, hydrology, and/or satellite remote sensing algorithms.
- Experience with land surface models (e.g., process-based geophysical models).
- Experience with phenology models and/or satellite-based surface property algorithms, such as leaf area index and albedo.
- Proficiency in at least one scientific programming language (e.g., exposure to Fortran preferred)
- Demonstrated publication record in relevant areas.
To Apply: Interested candidates should send a CV and a cover letter to jkenned@umd.edu explaining how your qualifications meet the posted requirements.