The Hunt for Missing Tornadoes: Using Satellite Imagery to Detect and Document Historical Tornado Damage in Canadian Forests

Historical tornado events from 1982 to 2020 were documented within Canada’s forested regions using high-resolution satellite imagery. Tornado forest disturbances were identified using a three-step process: 1) detecting, 2) assessing, and 3) dating each event. A grid of 120 km 3 120 km boxes was created covering Canada (excluding the extreme north). Of the 484 boxes, 367 were manually searched. Once a long, narrow region of tree damage was detected, it was first cross-referenced with known tornado databases to ensure it was a unique event. Once events were classified as either tornadic or downburst, the coordinates of the start, worst damage, and end locations were documented, as well as the direction of motion, damage indicators, degree of damage, estimated maximum wind speed, and F/EF-scale rating. In total, 231 previously unknown tornadoes were identified. In Ontario, 103 events were discovered, followed by 98 in Quebec, 9 in Manitoba, 6 in Saskatchewan, 9 in Alberta, 5 in British Columbia, and 1 in New Brunswick. The largest number of discovered tornadoes occurred in 2015, and the largest number of strong F2 tornadoes occurred in 2005. Most of the discovered tornadoes occurred in July for both F/EF1 and F/EF2 ratings. Most tornado tracks had widths between 200 and 400 m, and more than 50% of the tornadoes had a pathlength of less than 10 km. Of all the events that were discov-ered, 125 events could be fully dated, 19 were dated only by month, 41 were dated only by year, and 46 remained undated

Extreme Precipitation in the Eastern Canadian Arctic and Greenland: An Evaluation of Atmospheric Reanalyses

Extreme precipitation events are becoming more common in the Arctic as the climate warms, but characterizing these events is notoriously challenging. Atmospheric reanalyses have become popular tools for climate studies in data-sparse regions such as the Arctic. While modern reanalyses have been shown to perform reasonably well at reproducing Arctic climate, their ability to represent extreme precipitation events has not been investigated in depth. In this study, three of the most recent reanalyses, ERA-5, MERRA-2, and CFSR, are compared to surface precipitation observations in the Eastern Canadian Arctic and Greenland from 1980 to 2016 to assess how well they represent the most intense observed events. Overall, the reanalyses struggled to match observed accumulations from individual events (−0.11 ≤ r ≤ 0.47) but matched the observed seasonality of precipitation extremes. The region with the strongest match between observations and reanalyses was Southwest Greenland. Performance varies by event, and the best match between reanalyses and station observations may have a spatial/temporal offset (up to one grid cell or 1 day). The three products saw similar performance in general; however, ERA-5 tends to see slightly higher correlations and lower biases than MERRA-2 or CFSR. Considering the limitations of in situ observations, these results suggest that the reanalyses are capable of representing aggregate extreme precipitation (e.g., seasonal or annual time scales), but struggle to consistently match the timing and location of specific observed events

Blended Learning Makes Customizable Learning a Reality

There is no single best, one-size-fits-all blended learning model for every organization or every employee when developing soft skills in the workplace. Instead, a ‘mass customization’ approach that honours the uniqueness of different organizations, learning cultures, and learners can create highly personalized learning paths that enable each and every employee to learn. In this way, blended learning strategies can be used to maximize personal and collective learning in the workplace.York's Knowledge Mobilization Unit provides services and funding for faculty, graduate students, and community organizations seeking to maximize the impact of academic research and expertise on public policy, social programming, and professional practice. It is supported by SSHRC and CIHR grants, and by the Office of the Vice-President Research & Innovation. [email protected] www.researchimpact.c

Modeling the summertime evolution of sea-ice melt ponds

1] We present a mathematical model describing the summer melting of sea ice. We simulate the evolution of melt ponds and determine area coverage and total surface ablation. The model predictions are tested for sensitivity to the melt rate of unponded ice, enhanced melt rate beneath the melt ponds, vertical seepage, and horizontal permeability. The model is initialized with surface topographies derived from laser altimetry corresponding to first-year sea ice and multiyear sea ice. We predict that there are large differences in the depth of melt ponds and the area of coverage between the two types of ice. We also find that the vertical seepage rate and the melt rate of unponded ice are important in determining the total surface ablation and area covered by melt ponds

The 2015 Plains Elevated Convection at Night Field Project

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings

Analysis of a seeder-feeder and freezing drizzle event

Surface icing can cause dramatic consequences on human activities. What is more, numerical weather prediction models are not very accurate in determining freezing drizzle, which creates uncertainty when forecasting this type of weather phenomenon. Therefore, it is essential to improve the forecast accuracy of these models for such phenomena to mitigate risks caused by unforeseen freezing drizzle events. On 5 February 2012, an episode of freezing drizzle took place in the Guadarrama Mountains, at the center of the Iberian Peninsula. This episode was preceded by weak snowfall. After the freezing drizzle, moderate snowfall was recorded in the study area. This event was simulated using the Weather Research and Forecasting model. Through this analysis, we identified the meteorological factors at both synoptic scale and mesoscale that caused this episode. Wind perpendicular to an orographic barrier-generated updrafts and retention of moisture upwind, which caused orographic clouds to appear on the north side of the Guadarrama Mountains. Atmospheric stability prevented cloud formation at midlevels at the time of the freezing drizzle, which maintained cloud top temperatures warmer than −15ºC during the episode. The entrance of moisture and instability at midlevels caused cloud top temperatures substantially colder than −15º C, which coincided with snow in the mountain range. Cloud top temperature and thickness control the efficiency of the glaciation process, thereby determining the type of precipitation at the surface. Freezing drizzle risk and in-cloud icing algorithms were developed with the aim of predicting similar events in the study area, which could mitigate impacts on human activities.This paper was supported by the following grants: TEcoAgua, METEORISK PROJECT (RTC-2014-1872-5), Granimetro (CGL2010-15930) and CGL2011-25327 of MINECO, and LE220A11-2 and LE003B009 awarded by the Junta de Castilla y León

Projecting into the Future: the Canadian Arctic Environment, Tomorrow to 2100

Published as part of ArcticNet (2012). The article of record may be found at http://www.arcticnet.ulaval.ca/pdf/research/compendium2.pdf