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David M. L. Sills

The Meteorological Service of Canada held a series of three Forecasters Forum meetings between 2003 and 2005 to seek input from the meteorological community on the best ways to implement a restructuring strategy and to develop a common vision related to the provision of weather forecasts. The meeting provided significant insight into a number of topics related to operational forecasting in Canada and have added to the larger discussion on these issues in the international meteorological community.

During the course of the three forums, several themes emerged as overarching concerns. Foremost among them was the future role of the human forecaster. Most forum participants believed that human forecasters should be the “heart of weather prediction,” with an increased emphasis on the analysis/diagnosis/prognosis paradigm, and recommended developing the sophisticated tools required to facilitate that role.

Using results from the forums, it is suggested here that the primary role of the future forecaster should be to develop and maintain a sequence of plan-view composite depictions evolving through time to best represent the current and future states of the atmosphere. This would be accomplished using an area-based, object-oriented analysis/forecast system, with a toolbox of numerical weather prediction guidance and carefully designed artificial intelligence assistants. The forecaster's work would be focused on high-impact weather events, mainly in the short term but also in the longer term when necessary. Products would be automatically generated from the weather object database, allowing the forecast team to focus on “hands on” meteorology and maintaining shared situational awareness at all times.

Full access
Lisa S. Alexander
,
David M. L. Sills
, and
Peter A. Taylor

Abstract

The relationship between low-level mesoscale boundaries and convective storm initiation was investigated in southwestern Ontario, Canada. The influence of lake-breeze fronts, a type of boundary that frequently affects this region of the Great Lakes watershed in summer, presented a particular interest.

Radar data were processed using thunderstorm cell identification and tracking algorithms. The distances between the locations of storm cells reaching an intensity of 40 dBZ and the closest low-level mesoscale boundary were measured. Considering only days not influenced by a warm front, more than 75% of cells developed within 30 km of a low-level mesoscale boundary. Further examination by boundary type showed that cell initiations associated with moving boundaries and storm gust fronts occurred most often 0–5 km behind the boundaries. However, cell initiations associated with lake-breeze fronts most often occurred 0–5 km ahead of the boundaries. The analysis also suggested that lake-breeze fronts would often initiate the first storms of the day, which in turn generated gust fronts that could initiate subsequent storms.

Overall, the results were similar to a previous study investigating storm initiation in the vicinity of low-level mesoscale boundaries in eastern Colorado and include some new findings in relation to lake-breeze fronts. The findings can be used by forecasters as well as automated nowcasting algorithms in order to improve predictions of storm initiation.

Open access
Chun-Chih Wang
,
Daniel J. Kirshbaum
, and
David M. L. Sills

Abstract

Observations from the 2015 Environment and Climate Change Canada Pan/Parapan American Science Showcase (ECPASS) and real-case, cloud-resolving numerical simulations with the Weather Research and Forecasting (WRF) Model are used to investigate two cases of moist convection forced by lake-breeze convergence over southern Ontario (18 July and 15 August 2015). The two cases shared several characteristics, including high pressure conditions, similar morning soundings, and isolated afternoon convection along a line of lake-breeze convergence between Lakes Erie and Ontario. However, the convection was significantly stronger in the August case, with robustly deeper clouds and larger radar reflectivities than in the July case. Synoptic and mesoscale analyses of these events reveal that the key difference between them was their large-scale forcing. The July event exhibited a combination of strong warm advection and large-scale descent at midlevels (850–650 hPa), which created an inversion layer that capped cloud tops at 4–6 km. The August case exhibited similar features (large-scale descent and warm advection), but these were focused at higher levels (700–400 hPa) and weaker. As a consequence, the convection in the August case was less suppressed at midlevels and ascended deeper (reaching over 8 km). Although the subcloud updraft along the lake-breeze convergence zone was also found to be stronger in the August case, this difference was found to be an effect, rather than a cause, of stronger moist convection within the cloud layer.

Free access
Ibrahim Ibrahim
,
Gregory A. Kopp
, and
David M. L. Sills

Abstract

The current study develops a variant of the VAD method to retrieve thunderstorm peak event velocities using low-elevation WSR-88D radar scans. The main challenge pertains to the localized nature of thunderstorm winds, which complicates single-Doppler retrievals as it dictates the use of a limited spatial scale. Since VAD methods assume constant velocity in the fitted section, it is important that retrieved sections do not contain background flow. Accordingly, the current study proposes an image processing method to partition scans into regions, representing events and the background flows, that can be retrieved independently. The study compares the retrieved peak velocities to retrievals using another VAD method. The proposed technique is found to estimate peak event velocities that are closer to measured ASOS readings, making it more suitable for historical analysis. The study also compares the results of retrievals from over 2600 thunderstorm events from 19 radar–ASOS station combinations that are less than 10 km away from the radar. Comparisons of probability distributions of peak event velocities for ASOS readings and radar retrievals showed good agreement for stations within 4 km from the radar while more distant stations had a higher bias toward retrieved velocities compared to ASOS velocities. The mean absolute error for velocity magnitude increases with height ranging between 1.5 and 4.5 m s−1. A proposed correction based on the exponential trend of mean errors was shown to improve the probability distribution comparisons, especially for higher velocity magnitudes.

Open access
Patrick W. S. King
,
Michael J. Leduc
,
David M. L. Sills
,
Norman R. Donaldson
,
David R. Hudak
,
Paul Joe
, and
Brian P. Murphy

Abstract

Geostationary Operational Environmental Satellite (GOES) imagery is used to demonstrate the development of lake-breeze boundaries in southern Ontario under different synoptic conditions. The orientation of the gradient wind with respect to the shorelines is important in determining the location of such lines. When moderate winds (5–10 m s−1) are parallel to straight sections of coastlines, cloud lines can extend well inland. In the region between Lakes Huron and Erie lake-breeze lines merge frequently, sometimes resulting in long-lasting stationary storms and attendant heavy rain and flooding. The influence of the lakes is apparent in the tornado climatology for the region: tornadoes appear to be suppressed in regions visited by lake-modified air and enhanced in regions favored by lake-breeze convergence lines. The cloud patterns in the case of a cold front interacting with merging lake-breeze boundaries are shown to be similar to those on a major tornado outbreak day. Two of the cases discussed are used as conceptual models to explain many of the features in the patterns of tornado touchdown locations. In general, it appears that the lakes suppress tornadoes in southern Ontario, compared with neighboring states and in particular in areas where southwest winds are onshore, but enhance tornado likelihood locally in areas of frequent lake-breeze activity.

Full access
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
,
James W. Wilson
,
Paul I. Joe
,
Donald W. Burgess
,
Robert M. Webb
, and
Neil I. Fox

Abstract

Several severe thunderstorms, including a tornadic supercell, developed on the afternoon of 3 November 2000, during the Sydney 2000 Forecast Demonstration Project. Severe weather included three tornadoes, damaging wind gusts, hail to 7-cm diameter, and heavy rain causing flash flooding. A unique dataset was collected including data from two Doppler radars, a surface mesonet, enhanced upper-air profiling, storm photography, and a storm damage survey. Synoptic-scale forcing was weak and mesoscale factors were central to the development of severe weather. In particular, low-level boundaries such as gust fronts and the sea-breeze front played critical roles in the initiation and enhancement of storms, the motion of storms, and the generation of rotation at low levels. The complex and often subtle boundary interactions that led to the development of the tornadic supercell in this case highlight the need for advanced detection and prediction tools to improve the warning capacity for such events.

Full access
Vincent Y. S. Cheng
,
George B. Arhonditsis
,
David M. L. Sills
,
Heather Auld
,
Mark W. Shephard
,
William A. Gough
, and
Joan Klaassen

Abstract

The number of tornado observations in Canada is believed to be significantly lower than the actual occurrences. To account for this bias, the authors propose a Bayesian modeling approach founded upon the explicit consideration of the population sampling bias in tornado observations and the predictive relationship between cloud-to-ground (CG) lightning flash climatology and tornado occurrence. The latter variable was used as an indicator for quantifying convective storm activity, which is generally a precursor to tornado occurrence. The CG lightning data were generated from an 11-yr lightning climatology survey (1999–2009) from the Canadian Lightning Detection Network. The results suggest that the predictions of tornado occurrence in populated areas are fairly reliable with no profound underestimation bias. In sparsely populated areas, the analysis shows that the probability of tornado occurrence is significantly higher than what is represented in the 30-yr data record. Areas with low population density but high lightning flash density demonstrate the greatest discrepancy between predicted and observed tornado occurrence. A sensitivity analysis with various grid sizes was also conducted. It was found that the predictive statements supported by the model are fairly robust to the grid configuration, but the population density per grid cell is more representative to the actual population density at smaller resolution and therefore more accurately depicts the probability of tornado occurrence. Finally, a tornado probability map is calculated for Canada based on the frequency of tornado occurrence derived from the model and the estimated damage area of individual tornado events.

Full access
Vincent Y. S. Cheng
,
George B. Arhonditsis
,
David M. L. Sills
,
William A. Gough
, and
Heather Auld

Abstract

Destruction and fatalities from recent tornado outbreaks in North America have raised considerable concerns regarding their climatic and geographic variability. However, regional characterization of tornado activity in relation to large-scale climatic processes remains highly uncertain. Here, a novel Bayesian hierarchical framework is developed for elucidating the spatiotemporal variability of the factors underlying tornado occurrence in North America. It is demonstrated that regional variability of tornado activity can be characterized using a hierarchical parameterization of convective available potential energy, storm relative helicity, and vertical wind shear quantities. It is shown that the spatial variability of tornado occurrence during the warm summer season can be explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is clearly better at capturing the spatial variability of the cool season tornado activity. The results suggest that the Bayesian hierarchical modeling approach is effective for understanding the regional tornadic environment and in forming the basis for establishing tornado prognostic tools in North America.

Full 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
Full access