Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms

Abstract

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s−1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.

Recent Advances in Detection of Overshooting Cloud Tops From Longwave Infrared Satellite Imagery

“…Despite recent improvements and science applications with the Bedka and Khlopenkov (2016) method (e.g., Apke et al., 2018; Sandmael et al., 2019), the launch of GOES‐16 and ‐17 satellites and application of this method to 1‐min ABI data (Schmit et al., 2017) revealed some inconsistencies in detection performance from image to image, suggesting that further advancements were needed. This paper provides an enhanced technical description and describes recent advancements to IR‐based deep convection and OT detection algorithms since the version described by Bedka and Khlopenkov (2016).…”

Characteristics of hail hazard in South Africa based on satellite detection of convective storms

“…This method was found to perform much better than the Bedka et al (2010) version used in previous climatology studies. False OT detections in very cold outflow near to actual OT regions is the most common source of error, though Sandmael et al (2019) showed that agreement between OT detections and severe weather conditions can be further improved using a combination of IR and visible wavelength signals.…”

Backward Adaptive Brightness Temperature Threshold Technique (BAB3T): A Methodology to Determine Extreme Convective Initiation Regions Using Satellite Infrared Imagery

“…A useful proxy to identify the presence of deep convection development inside a large cloud shield is the presence of an overshooting cloud top (OT) that indicates an updraft strong enough to penetrate through the tropopause. A variety of studies conducted in different locations have shown that OTs are associated with severe weather such as strong winds, heavy rainfall, hail or tornadoes [32][33][34][35][36][37].…”