Elisa Murillo-August 31

The use of remote sensing observation for severe weather detection.

Radar observations have been used in an abundance of studies aiming to identify severe weather hazards. However, due to historical reporting limitations, hail events have not been studied as extensively as alternative hazards. In the contiguous US, real-time analysis and detection of severe storms is largely conducted using ground-based radar observations, es- pecially those from the operational Next Generation Weather Radar network (NEXRAD), which provides three-dimensional information on the physics and dynamics of storms at 5-min intervals. Recent NEXRAD upgrades to higher resolution and dual-polarization ca- pabilities (completed in 2013) have provided improved capability to discriminate between hydrometeor types in real time. New generation geostationary satellite observations have also led to significant changes in observing capabilities over the US beginning with the launch of GOES-16. The spatiotemporal resolution of geostationary satellite imagery over the US is now greater than that of NEXRAD. Additionally, the improvements to satellite observations enable better identification of above anvil cirrus plumes (AACPs), which are generated from intense tropopause-penetrating updrafts and gravity wave breaking. AACPs are often evident during severe weather outbreaks and, due to their importance, have been studied for several decades.

To date, thorough assessments of the wide range available products’ ability to iden- tify hail occurrence (and hail size) have been uncommon or, for some variables, do not exist. Additionally, current published research indicates that the AACP signature is one of the strongest indicators of a severe storm depicted by visible and IR satellite imagery. De- spite the extensive research devoted to the AACP, there is lingering uncertainty regarding how these processes are depicted by datasets routinely used in operations and how AACP recognition can augment severe storm identification. This study undergoes a comprehen- sive comparative analysis to address two main objectives: a) Thoroughly assess available radar, satellite, lightning and based products’ ability to identify hail events and b) quantify the relationship between AACPs and all severe weather.

For hail detection assessment, we compare the most commonly used objective hail identification methods and less common approaches, focusing on quantitative metrics from both satellite and radar observations over the CONUS and found that HDR below the melt- ing level (BML), MEHS, and VIL Density provide the best hail event indication, while a combination of either MEHS or VIL Density combined with HDR BML provide the best hail-producing storm discrimination. The MEHS power relation was also refit to the study’s report dataset, and outperformed the Witt et al. (1998a) MEHS for the maximum estimated hail size.

AACP and non-AACP storms are linked with all severe weather identification prod- ucts and severe weather and found that AACP storms are much more likely to be severe than non-AACP storms, and the majority of significant severe weather reports are produced by AACP storms. When specifically focused on significant hail, the presence of an AACP can increase forecaster confidence that 2+ inch hail will occur when issuing a warning.