High-Resolution Hail Observations: Implications for NWS Warning Operations and Climatological Data

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Tuesday, 4 February 2014: 11:30 AM
Room C201 (The Georgia World Congress Center )
Scott F. Blair, NOAA/NWS, Pleasant Hill, MO; and D. E. Cavanaugh, J. M. Laflin, and J. W. Leighton

Storm reports are a critical component of the National Weather Service (NWS) warning decision process by providing "ground-truth" for ongoing weather events, and by helping forecasters calibrate radar signatures in real-time. Unfortunately, NWS offices may receive reports on an irregular basis, potentially due to several limiting factors including (but not limited to): population density, time of day, and spotter availability. One operational challenge that arises when storm reports lag is the difficulty of discerning whether this absence of reports infers a weakening or sub-severe storm or a shortcoming in the reporting network - and overreliance on or incorrect interpretation of a lack of storm reports can result in degraded warning service to the end-user. Additionally, the inconsistent and low-resolution nature of these reports is often reflected in the severe weather climatological database published in Storm Data.

New operationally-relevant perspectives of hail observations are revealed by the examination of approximately 30 unique cases of high-resolution hail datasets acquired from the ongoing field research project, A Hail Spatial and Temporal Observing Network Effort (HailSTONE). These high-resolution hail observations provide tremendous spatiotemporal insight into the hail-fall character of convective storms, and mitigate the inherent limitations of Storm Data. This study provides a preliminary investigation of both real-time and post-event hail reports available to operational meteorologists, and compares these reports to high-resolution hail observations collected by HailSTONE. The detailed and high-quality data resolution also affords an opportunity to verify the maximum hail size forecast in NWS warnings and statements. In this study, observed size biases in NWS products and in the historical hail database are explored, as is current radar-based conceptual models for hail size prediction.