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John Hanesiak
,
Mark Melsness
, and
Richard Raddatz

Abstract

High-temporal-resolution total-column precipitable water vapor (PWV) was measured using a Radiometrics Corporation WVR-1100 Atmospheric Microwave Radiometer (AMR). The AMR was deployed at the University of Manitoba in Winnipeg, Canada, during the 2003 and 2006 growing seasons (mid-May–end of August). PWV data were examined 1) to document the diurnal cycle of PWV and to provide insight into the various processes controlling this cycle and 2) to assess the accuracy of the Canadian regional Global Environmental Multiscale (GEM) model analysis and forecasts (out to 36 h) of PWV. The mean daily PWV was 22.6 mm in 2003 and 23.8 mm in 2006, with distinct diurnal amplitudes of 1.5 and 1.8 mm, respectively. It was determined that the diurnal cycle of PWV about the daily mean value was controlled by evapotranspiration (ET) and the occurrence/timing of deep convection. The PWV in both years reached its hourly maximum later in the afternoon as opposed to at solar noon. This suggested that the surface and atmosphere were well coupled, with ET primarily being controlled by the vapor pressure deficit between the vegetation/surface and atmosphere. The decrease in PWV during the evening and overnight periods of both years was likely the result of deep convection, with or without precipitation, which drew water vapor out of the atmosphere, as well as the nocturnal decline in ET. The results did not change for days on which low-level winds were light (i.e., maximum winds from the surface to 850 hPa were below 20 km h−1), which supports the notion that the diurnal PWV pattern was associated with the daily cycles of local ET and convection/precipitation and was not due to advection. Comparison of AMR PWV with the Canadian GEM model for the growing seasons of 2003 and 2006 indicated that the model error was 3 mm (13%) or more even in the first 12 h, with mean absolute errors ranging from 2 to 3.5 mm and root-mean-square errors from 3 to 4.5 mm over the full 36-h forecast period. It was also found that the 3–9-h forecast period of GEM had better error scores in 2006 than in 2003.

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Joanne Kunkel
,
John Hanesiak
, and
David Sills

Abstract

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 × 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 discovered, 125 events could be fully dated, 19 were dated only by month, 41 were dated only by year, and 46 remained undated.

Open access
Xin Jin
,
John M. Hanesiak
, and
David G. Barber

Abstract

The time series of daily averaged cloud fractions (CFs) collected from different platforms—two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP) model, a Vaisala 25K laser ceilometer, and ground-based manual observations (manobs)—above the winter camp of the Canadian Arctic Shelf Exchange Study (CASES) field experiment are analyzed in this study. Taking the manobs as standard, the authors conclude that 1) the NCEP products considerably underestimated CFs in spring (e.g., from April to May) and 2) the performance of two MODIS products depends on the variation of solar zenith angle (SZA). Aqua MODIS misrepresents the snow-covered surface as clouds with almost randomly distributed CFs during the dark winter [cos(SZA) < 0], leading to the overestimation of CFs in winter while Terra MODIS has good agreement with manobs. When 0.1 < cos(SZA) < 0.4, both MODIS products regularly misrepresent the snow-covered background as clouds, leading to the significant overestimation of CFs in late winter (February) and early spring (March). When cos(SZA) > 0.4, both MODIS products have good performance in detecting cloud masks over snow backgrounds. If the sky is slightly cloudy, surface-based meteorological observers tend to underestimate cloud amounts when there is a lack of light. Comparing the CFs from Terra and manobs, the authors conclude that this bias can be over 10%. Power spectral analysis and wavelet analysis show three results: 1) High clouds more frequently appear in winter than in spring with periods between 8 and 16 days, indicating their close connection with synoptic events. Current NCEP products can predict this periodicity but have a phase lag. 2) Middle and low clouds are more local and are common in mid- and late spring (April and May) with periods between 2 and 4 days. At the CASES winter and spring field site, the periodicity of high clouds is dominant. 3) The time-scale-dependent correlation coefficients (CCs) between both MODIS products, NCEP and manobs, show that with high frequent CF sampling per day, the CCs are stable when the time scale varies between 1 and 4 days: with Terra MODIS and NCEP, the value is about 0.6; with Aqua MODIS, between 0.4 and 0.5. All CCs get smaller when the time scale increases beyond 8 days: with respect to both MODIS products, the CCs get closer with values between 0.3 and 0.4; with respect to NCEP, the CC dramatically decreases from positive values to negative values, indicating the lack of accuracy in current NCEP cloud schemes.

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