University Corp. For Atmospheric Research (UCAR) Post Doc Fellow - Modeling Hydrologic Influence Of Agricultu in Boulder, Colorado

Job Title: Post-Doctoral Fellow I - Modeling hydrologic influence ofagriculture managementLocation: Boulder, CoType: Full-time, ExemptPosition Term: 2 - Year TermApplication Deadline:This position will be posted until a sufficient number of applications havebeen received. Thereafter, applications will be reviewed on an as-neededbasis.Relocation: No relocation package is offered for this position.Work Authorization: UCAR/NCAR will sponsor a work visa to fill this position.Where You Will Work:Located in Boulder, Colorado, the National Center for Atmospheric Research(NCAR) is one of the world's premier scientific institutions, with aninternationally recognized staff and research program dedicated to advancingknowledge, providing community-based resources, and building humancapacity in the atmospheric and related sciences. NCAR is sponsored by theNational Science Foundation (NSF) and managed by the UniversityCorporation for Atmospheric Research (UCAR).The Research Applications Laboratory (RAL) conducts directed research thatcontributes to the fundamental understanding of the atmosphere and relatedphysical, biological, and social systems; to support, enhance, andextend the capabilities of the scientific community, and to develop andtransfer knowledge and technology for the betterment of life on Earth.What You Will Do:This is a new, full-time, 2-year term position. The new hire will conductresearch on enhancing the prediction capabilities of the National Water Model(NWM) by establishing a modeling framework to represent the hydrologicinfluence of agriculture management processes in the NWM. This research willinvolve the incorporation of new modules of crop-growth, irrigation, andtile-drainage, and the enhanced use of satellite data. The new hire willconduct multi-year NWM reforecasts in a "near-operational" environment andusing the Office of Water Prediction (OWP) evaluation metrics to assessthe performance of the new crop-modeling options in NWM against currentoperational NWM prediction capability; will focus on understanding theinterplay among crop growth, irrigation, runoff, soil moisture, andstreamflow, and on the merits of adding and optimizing agriculturemanagement cResponsibilities:Conduct research aimed at improving NWM prediction capabilities andunderstanding of the interplay among crop growth, irrigation, runoff,soil moisture, and streamflow in the context of national water forecasts.The work will include:-- implement the Noah-MP crop-growth model, irrigation, and tile-drainagein NWM;-- implement agriculture management data in NWM data input;-- conduct multi-year NWM reforecasts in a "near-operational" environmentwith new agriculture management modeling components in NWM;-- benchmark performance of the new modeling capabilities using the Office ofWater Prediction (OWP) evaluation metrics;-- optimize the new modeling capabilities in NWM.Communicate research results through publication in peer-reviewed journals,meeting proceedings, and presentations at scientific meetings.Participate in research development with internal and external collaboratorsacross disciplines.What You Need:Education and Years of Experience:Ph.D. degree in atmospheric sciences or hydrology with expertise inland-surface and hydrology modeling and physical parameterization studies.Knowledge, Skills, and Abilities:Demonstrated skill in conducting and analyzing land-surface and hydrologyprediction model simulations, particularly evaluation of simulated watercycle components against observations.Demonstrated skill in quantifying model uncertainty and parameteroptimization.In-depth knowledge of issues surrounding land-surface and hydrology processesand modeling.Ability to develop or modify land/hydrology parameterization schemes in aversion control environment; conduct and manage large simulations.Demonstrated ability to publish research results in peer-reviewed journals.Ability to effectively participate in and interact productively with modeldevelope