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Bernadette H. Connell
and
David R. Miller

Abstract

The authors review sources of error in radiosonde measurements in the atmospheric boundary layer and analyze errors of two radiosonde models manufactured by Atmospheric Instrumentation Research, Inc. The authors focus on temperature and humidity lag errors and wind errors. Errors in measurement of azimuth and elevation angles and pressure over short time intervals and at higher attitudes introduce wind vector errors greater than 5 m s−1. Mean temperature and humidity lag errors were small, and collectively, these tag errors had little effect on the calculation of the vertically integrated water vapor content. However, individual large lag errors occurred with dramatic changes in the environment, such as near the surface or at the top of the boundary layer. Dual-sonde flights showed mean instrument error comparable to lag error and had little effect on the calculation of the columnar water vapor content. A hypothetical consistent error of 5% in the measurement of relative humidity in a dry environment could introduce error in the calculation of columnar water vapor content up to 1 kg m−2.

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Connell S. Miller
,
Gregory A. Kopp
,
David M.L. Sills
, and
Daniel G. Butt

Abstract

Currently, the Enhanced Fujita scale does not consider the wind-induced movement of various large compact objects such as vehicles, construction equipment, farming equipment / haybales, etc. that are often found in post-event damage surveys. One reason for this is that modelling debris in tornadoes comes with considerable uncertainties since there are many parameters to determine, leading to difficulties in using trajectories to analyze wind speeds of tornadoes. This paper aims to develop a forensic tool using analytical tornado models to estimate lofting wind speeds based on trajectories of large compact objects. This is accomplished by implementing a Monte Carlo simulation to randomly select the parameters and plotting cumulative distribution functions showing the likelihood of lofting at each wind speed. After analyzing the debris lofting from several documented tornadoes in Canada, the results indicate that the method provides threshold lofting wind speeds that are similar to the estimated speeds given by other methods. However, the introduction of trajectories produces estimated lofting wind speeds that are higher than the EF-scale rating given from the ground survey assessment based on structural damage. Further studies will be required to better understand these differences.

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Daniel G. Butt
,
Aaron L. Jaffe
,
Connell S. Miller
,
Gregory A. Kopp
, and
David M. L. Sills

Abstract

In many regions of the world, tornadoes travel through forested areas with low population densities, making downed trees the only observable damage indicator. Current methods in the EF scale for analyzing tree damage may not reflect the true intensity of some tornadoes. However, new methods have been developed that use the number of trees downed or treefall directions from high-resolution aerial imagery to provide an estimate of maximum wind speed. Treefall Identification and Direction Analysis (TrIDA) maps are used to identify areas of treefall damage and treefall directions along the damage path. Currently, TrIDA maps are generated manually, but this is labor-intensive, often taking several days or weeks. To solve this, this paper describes a machine learning– and image-processing-based model that automatically extracts fallen trees from large-scale aerial imagery, assesses their fall directions, and produces an area-averaged treefall vector map with minimal initial human interaction. The automated model achieves a median tree direction difference of 13.3° when compared to the manual tree directions from the Alonsa, Manitoba, tornado, demonstrating the viability of the automated model compared to manual assessment. Overall, the automated production of treefall vector maps from large-scale aerial imagery significantly speeds up and reduces the labor required to create a Treefall Identification and Direction Analysis map from a matter of days or weeks to a matter of hours.

Significance Statement

The automation of treefall detection and direction is significant to the analyses of tornado paths and intensities. Previously, it would have taken a researcher multiple days to weeks to manually count and assess the directions of fallen trees in large-scale aerial photography of tornado damage. Through automation, analysis takes a matter of hours, with minimal initial human interaction. Tornado researchers will be able to use this automated process to help analyze and assess tornadoes and their enhanced Fujita–scale rating around the world.

Open access
David M. L. Sills
,
Gregory A. Kopp
,
Lesley Elliott
,
Aaron Jaffe
,
Elizabeth Sutherland
,
Connell Miller
,
Joanne Kunkel
,
Emilio Hong
,
Sarah Stevenson
, and
William Wang
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David M. L. Sills
,
Gregory A. Kopp
,
Lesley Elliott
,
Aaron L. Jaffe
,
Liz Sutherland
,
Connell S. Miller
,
Joanne M. Kunkel
,
Emilio Hong
,
Sarah A. Stevenson
, and
William Wang

Abstract

Canada is a vast country with most of its population located along its southern border. Large areas are sparsely populated and/or heavily forested, and severe weather reports are rare when thunderstorms occur there. Thus, it has been difficult to accurately assess the true tornado climatology and risk. It is also important to establish a reliable baseline for tornado-related climate change studies. The Northern Tornadoes Project (NTP), led by Western University, is an ambitious multidisciplinary initiative aimed at detecting and documenting every tornado that occurs across Canada. A team of meteorologists and wind engineers collects research-quality data during each damage investigation via thorough ground surveys and high-resolution satellite, aircraft, and drone imaging. Crowdsourcing through social media is also key to tracking down events. In addition, NTP conducts research to improve our ability to detect and accurately assess tornadoes that affect forests, cropland, and grassland. An open data website allows sharing of resulting datasets and analyses. Pilot investigations were carried out during the warm seasons of 2017 and 2018, with the scope expanding from the detection of any tornadoes in heavily forested regions of central Canada in 2017 to the detection of all EF1+ tornadoes in Ontario plus all significant events outside of Ontario in 2018. The 2019 season was the first full campaign, systematically collecting research-quality tornado data across the entire country. To date, the project has found 89 tornadoes that otherwise would not have been identified, and increased the national tornado count in 2019 by 78%.

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