A set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (November–March). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological snow ratio. Standard verification methods (mean, median, bias, and root-mean-square error) and a new method that places the forecasts in the context of municipal snow removal, and introduces the concept of forecast credibility, are used. Results suggest that the neural network method performs best for individual events, owing in part to the inverse relationship between melted liquid equivalent and snow ratio; hence, the ongoing difficulty of producing accurate forecasts of melted equivalent precipitation (a problem in all seasons) is compensated for rather than amplified when converting to snowfall amounts. This analysis should be extended to a larger selection of reports, which is anticipated in conjunction with efforts currently ongoing at the National Oceanic and Atmospheric Administration’s Hydrometeorological Prediction Center.
Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist that discuss the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979–1980 and 2017–2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Re-analysis (NARR). The RRV experiences, on average, 2.6 events per year. Blizzard frequency is bimodal, with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. The objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (eight-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects.
On the evening of 18 July 2004, several tornadoes occurred with two supercell thunderstorms over eastern North Dakota. The second and smaller in diameter of these storms produced an F4 tornado in an environment with lifting condensation level (LCL) heights that were atypically high according to recent statistical studies about supercell tornado environments. Surface dewpoints were also underforecast by computer models. These two issues are examined in this paper, which provides an overview of this event. The synoptic setting and environment characteristics suggest that evapotranspiration (ET) was responsible in part for enhancing surface moisture. It is likely that ET affected instability and convection initiation. This study also found that the presence of steep low-level lapse rates juxtaposed with low-level convective available potential energy along a surface trough may have contributed to tornado development in a high LCL environment where wind and instability characteristics were otherwise favorable for supporting supercell tornadoes.
The objective of this study is to provide guidance on when hail and/or wind is climatologically most likely (temporally and spatially) based on the ratio of severe hail reports to severe wind reports, which can be used by National Weather Forecast (NWS) forecasters when issuing severe convective warnings. Accordingly, a climatology of reported hail-to-wind ratios (i.e., number of hail reports divided by the number of wind reports) for observed severe convective storms was derived using U.S. storm reports from 1955 to 2017. Owing to several temporal changes in reporting and warning procedures, the 1996–2017 period was chosen for spatiotemporal analyses, yielding 265 691 hail and 294 449 wind reports. The most notable changes in hail–wind ratios occurred around 1996 as the NWS modernized and deployed new radars (leading to more hail reports relative to wind) and in 2010 when the severe hail criterion increased nationwide (leading to more wind reports relative to hail). One key finding is that hail–wind ratios are maximized (i.e., relatively more hail than wind) during the late morning through midafternoon and in the spring (March–May), with geographical maxima over the central United States and complex/elevated terrain. Otherwise, minimum ratios occur overnight, during the late summer (July–August) as well as November–December, and over the eastern United States. While the results reflect reporting biases (e.g., fewer wind than hail reports in low-population areas but more wind reports where mesonets are available), meteorological factors such as convective mode and cool spring versus warm summer environments also appear associated with the hail–wind ratio climatology.
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