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90E Project Summary The Department of Geography and Spatial Sciences at the University of Delaware is seeking a Postdoctoral Researcher to characterize current and future U.S. severe convective storm (SCS) risk for insurance applications. The project goals are to (1) estimate present-day and future SCS risk using simulations from large ensemble systems and (2) identify and quantify the uncertainty in historical trends and near-term model projections. The work will be in close collaboration with scientists at Columbia University and NOAA Geophysical Fluid Dynamics Lab as well as industry professionals across the (re)insurance sector to develop SCS risk assessments and decision-making tools/frameworks using near-term SCS projections.

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Severe Weather, Climate Risk Background Severe convective storms (SCS) - thunderstorms that produce damaging winds, large hail, or tornadoes - are increasingly becoming a dominant driver of insured losses. Consequently, assessments of present-day as well as projected future SCS risk are of considerable interest and value to the insurance industry. Our group will use historical and future simulations in large ensembles to better resolve risk from SCS hazards like tornadoes and hail, and to tailor risk assessments and near-term SCS projections to meet the needs of the insurance industry. Responsibility Develop data-driven models to simulate severe weather hazards like tornadoes and hail, and apply models to investigate severe weather hazard activity in large ensemble simulations. Evaluate model biases, e.g. from GFDL's Seamless System for Prediction and EArth System Research (SPEAR) large ensembles. Identify and explore sources of uncertainty in near-term SCS projections, e.g., from interannual to decadal climate variability. Publish findings on peer-reviewed journals, present progress at professional meetings, prepare project reports and deliverables, and collaborate with other research and industry teams on a regular basis. Qualifications Ph. D. in a relevant field (e.g., Atmospheric Science, Meteorology, Climate Science, Geography, or related areas) or should be completed with their Ph. D. degree requirements by January 2027. Experience with weather and/or climate model output (e.g., SPEAR, WRF, CESM), and familiarity with climate model frameworks (e.g., large ensembles). Proficiency in programming and data analysis (e.g., Python) and handling large datasets, such as from hindcasts, large ensembles, or Coupled Model Intercomparison Project. Strong written and verbal communication skills. Ability to work independently as well as effectively in a collaborative, multidisciplinary team environment. Preferred Skills Experience with data-driven (including machine learning), physics-informed modeling approaches. Background in climate risk, i.e., applying weather and climate to societal impacts, decision-making, adaptation, etc. Familiarity with high-performance computing. This position is in-person with support for conference travel and other professional development activities. To Apply Any questions can be directed to Dr. Kelsey Malloy; kmmalloy@udel.edu