UPCOMING POSITION: Postdoctoral Fellow focusing on the assimilation of imaging spectroscopy data to improve the representation of vegetation dynamics in ecosystem models within the Terrestrial Ecosystem Science and Technology (TEST; bnl.gov/test/) group at Brookhaven National Laboratory (BNL). This NASA-funded project focuses on the integration of remote-sensing data, specifically high-spectral resolution field and imaging spectroscopy data, within an efficient model-data assimilation framework, to improve the characterization of vegetation dynamics in terrestrial ecosystem models. The project is part of the larger predictive ecosystem analyzer (PEcAn) scientific workflow system (pecanproject.org), which aims to make ecosystem models, data assimilation, and forecasting more accessible, automated, and repeatable. The primary objective is to comprehensively examine the potential for direct assimilation of optical remote sensing observations into sophisticated ecosystem models, starting with the Ecosystem Demography 2.2 model, to better initialize and constrain projections of surface energy balance, vegetation composition, and carbon pools and fluxes. The project combines remote sensing, radiative transfer modeling, ecosystem modeling, and advanced statistical and computation approaches to diagnose the drivers of spatial and temporal variability in the terrestrial carbon cycle and the sources of uncertainty in these estimates. The initial focus of the project is the temperate/boreal transition zone in northern Wisconsin, a region that is expected to show large climate change responses and is one of the most data-rich regions in the country, but will expand in scope to sites around the continental U.S. in years 2 and 3. The results from this project will provide an important step toward the operational capacity to assimilate reflectance observations, uniformly, within sophisticated ecosystem models with the goal of constraining model projections of energy, water, and carbon pools and fluxes of terrestrial ecosystems. Qualifications: Required qualifications are a doctoral degree in a relevant ecological, environmental, or computer science field. The ideal candidate would have experience with more than one of the following areas: ecosystem process models, remote sensing data, imaging spectroscopy (i.e. hyperspectral) data, radiative transfer modeling in the optical domain, open-source programming environments (e.g. R, Python), linux, data assimilation, scientific writing. Application Process: Interested applicants are encouraged to submit a cover letter, CV, and contact info for 3 references to Dr. Shawn Serbin ([email protected]). Review of applications will begin on October 30th but will remain open until filled.
Posted on: Tue, 02 Sep 2014 21:01:17 +0000
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