Thursday, 11 January 2018: 2:15 PM
Room 12B (ACC) (Austin, Texas)
Large hail can lead to significant agricultural and property loss. Due to historical limitations of reporting and other factors, these events have not been studied as extensively as other hazards such as tornadoes. In the contiguous US, real-time analysis and detection of severe storms is largely based on information gathered by the ground-based NEXRAD (Next-Generation Radar) network, which obtains volumes at about 5-min intervals. Since 2013, the NEXRAD network has provided radar observations at dual-polarization, which has provided better indications of hydrometeor types and of hail events. After the launch of GOES-16 in November of 2016, the spatial and temporal resolution of geostationary satellite imagery over the US is now greater than that of the NEXRAD network. It is expected that this data will enable improvements in severe weather prediction. Previous studies have found several satellite signatures associated with deep and severe convection. For example, overshooting tops (OTs) show a clear and distinct updraft in visible imagery with a corresponding local minimum in infrared brightness temperatures, indicative of high cloud tops. Severe storms are commonly associated with OTs, but only ~1% of OT-producing storms are severe. On the other hand, above-anvil cirrus plumes, which are collocated with OTs and produce the cloud-top IR signatures referred to in older literature as the “enhanced-V” or “cold-ring”, have recently been shown to be one of the most powerful satellite-based indicators of severe weather. Mesoscale atmospheric motion vector (mAMV) products have recently been used with 1-min GOES imagery to diagnose unique cloud-top motions that often precede severe weather. These satellite products, as well as potentially new information offered solely by GOES-16, may show improvement in identifying and predicting severe storms. However, to date, an assessment of a broad range of radar- and satellite-based products’ ability to identify and predict hail events has been incomplete.
This study aims to determine the ability, from a statistical approach, of numerous satellite- and radar-based products to detect and predict large hail events. First, radar-based products are evaluated based on hail reports to determine the best radar indication of hail occurrence. Second, both reports and the most skillful radar-based indicator will be used to assess the value of radar and satellite products for hail identification and prediction. Using 5-min NEXRAD data from more than 20 recent severe weather events (2013-Present) and more than 5,000 storms, we will show that differential radar reflectivity observations below the melting level can be confidently used as a proxy for severe hail occurrence. The predictive skill of additional radar- and satellite-based products (from 1-min GOES-14 imagery) will also be shown relative to both the hail reports and radar-based hail indication.
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