Evaluation of New Datasets & Breakthrough Identification

Project Summary

There is a need for improved data resources for our region that capture important lake-land-atmosphere feedback and data on extremes, especially precipitation extremes. We will apply our current model evaluation framework (Briley et al. 2021) as part of our Great Lakes Ensemble project to continue asking fundamental questions of new datasets that become available, including observations, projections, and simulations produced for research. Our priority will be assessing the new high-resolution projections, selecting CMIP6 models that offer potentially promising advancements in simulation quality. Our goal is to determine if new datasets offer improved information for the GL region, including if and how the GL and lake-effect processes are represented, if hydrometeorological coupling is improved, and if bias is reduced. We will expand our evaluation framework to identify when there are scientific breakthroughs in terms of more robustly representing important physical processes for the region (e.g., lake effects, summertime mesoscale convective complexes, net basin supply components) by developing a series of questions and metrics. Our ‘breakthrough identification’ framework is an innovative approach to streamlining dataset evaluation and ultimately for identification of new and improved data resources. This research will serve not only our stakeholders, but also the broader scientific community by directing researchers and users to new credible data sources. Research question: Are new, emerging datasets exhibiting improved data credibility for our region?