Undergraduate Research Opportunity
Drs. Aaron Johnson and Xuguang Wang from the Multiscale Data Assimilation and Predictability (MAP) lab have a research opportunity for an undergraduate. Machine Learning techniques such as Random Forests (RF) have a demonstrated ability to provide skillful severe weather forecasts in the context of post-processing convection-allowing ensemble (CAE) forecasts. This opportunity will consist of a project related to optimization and application of RF calibration of MAP’s CAE forecasts from the 2018 and 2019 Hazardous Weather Testbed Spring Forecast Experiments in the context of generating sub-daily (4-hour) convective outlooks of severe weather. The research will include applying the RF model with different predictor variables to understand and quantify the impact on the skill of RF-based predictions of specific severe weather hazards, sensitivity to training sample size, and/or the role of flow-dependence in optimizing the RF model performance. For interested candidates, please email firstname.lastname@example.org and email@example.com with your transcript and CV.