Gabbie's Presentation:
The Spatial Area and other Attributes of Overshooting Tops as Indicators of Hail Size as Viewed by GOES-16
Each year hail contributes to most of the aggregated loss from severe weather-induced property and crop damages in the U.S. Low-altitude radar coverage is also poor in some areas of the U.S., which frequently yields hail size overestimation. Prior work investigating overshooting tops (OTs) using satellite data has shown that overshooting top area (OTA) is correlated with tornado intensity. This work builds off the OT-tornado research to explore possible relationships between OT characteristics and hail size observed at the ground. This relationship could help to alleviate the deficiencies of current hail size estimation methods.
All hail reports from 2018 through 2022 across the contiguous U.S are assigned to 0.5° x 0.5° latitude-longitude grid cells, from which the largest hail report per hour per grid cell is selected. An OT detection algorithm is applied to the selected hail reports to find the nearest OT to the report. OT characteristics including area (OTA), brightness temperature difference (BTD), and overshooting top depth (OTD) are considered for relationships with observed hail size at the ground.
Results show that OTA decreases linearly and OTD increases linearly with increasing hail size. These relationships are most pronounced from April-September (warm season) in the southern Great Plains region. These results could be helpful for forecasters issuing severe weather warnings and, more specifically, identifying the likelihood for severe hail when radar coverage is poor or not available. Future work with idealized numerical modeling simulations will help provide physical insight into these findings.
Zach's Presentation:
Parametric and structural uncertainties in modeling dry deposition of atmospheric aerosol particles
Emissions of aerosol particles and their precursor gases have increased markedly as a result of human activity relative to pre-industrial times, and the aerosol indirect effect represents the single largest uncertainty in effective radiative forcing estimates. To begin shrinking this uncertainty, it is pivotal to first understand the individual sources of uncertainty associated with modeling aerosols. Since aerosol microphysical processes occur on scales much smaller than the size of most model grid cells and are strongly influenced by subgrid-scale variability, oftentimes a handful of simplified equations and fitted parameters are used to represent these processes. Parametric uncertainty refers to the uncertainty associated with the choice of model parameters. There are also many distinct mathematical representations used to characterize the aerosol population within a given model, and structural uncertainty represents the uncertainty incurred from simply employing a specific aerosol model configuration. Previous modeling studies have found dry deposition, the removal of particles without the assistance of precipitation, to be one of the largest sources of parametric uncertainty in estimates of global CCN concentrations within a single global aerosol model framework. In this talk, I will show comparisons of the structural and parametric uncertainties associated with modeling this specific removal process obtained by implementing two dry deposition parameterizations in two aerosol model structures commonly used in global models.
