Examining whiteness (in) education is a journey of identity and materiality. In this article, the authors adopt an approach to research that highlights the role of performance in constituting identity. They believe that theorizing identity in this way is facilitated through autoethnographic storytelling, which allows one to theorize material performances of identity, along with the culture in which those performances are situated. In the following, the authors dialogically theorize whiteness education through their stories. They draw upon themselves as individuals and one another as partners in humanity in order to make sense of education within a context of whiteness. They develop community autoethnography as a method with which to engage in such dialogical theorization.

The multi-radar/multi-sensor (MRMS) system generates an operational suite of derived products in the NationalWeather Service useful for real-time monitoring of severe convective weather. One such product generated byMRMSis the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0 °C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥ 25.4 mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous U.S. from 2012–2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density.