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Nathan M. Hitchens

Abstract

The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.

Significance Statement

The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.

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Zachary F. Johnson and Nathan M. Hitchens

Abstract

The dryline is among the most important meteorological phenomena in the Great Plains because of its significance in tornadogenesis, severe weather, and consistent rainfall. Past research has extensively examined the dynamics of the dryline; however, recent meteorological research looks beyond dynamics and focuses on land–atmosphere interactions. This study focuses on how soil moisture, a surrogate for evapotranspiration, affects the climatological longitudinal positioning of the dryline, presenting a climatological study for the months of April–June during 2006–15 in the southern Great Plains. Here, drylines are defined as specific humidity gradients exceeding 3 g kg−1 (100 km)−1 that do not deviate more than 30° from a north–south orientation; they were found to occur on 33.4% of spring days, and the most favorable position was −100.9° at 0000 UTC. Specific humidity gradients ranged from 3.0 to 15.2 g kg−1 (100 km)−1, with an average value of 6.8 g kg−1 (100 km)−1. A relationship between the dryline longitudinal position and soil moisture was found; as soil moisture values increased, the dryline was located farther west, which suggests soil moisture may influence the longitudinal positioning of the dryline. There was also a relationship between the gradient of soil moisture and the intensity (specific humidity gradient) of the dryline, such that when longitudinal soil moisture gradients were strong (increasing from west to east), the dryline intensity increased.

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Nathan M. Hitchens and Harold E. Brooks

Abstract

The Storm Prediction Center issues four categorical convective outlooks with lead times as long as 48 h, the so-called day 3 outlook issued at 1200 UTC, and as short as 6 h, the day 1 outlook issued at 0600 UTC. Additionally, there are four outlooks issued during the 24-h target period (which begins at 1200 UTC on day 1) that serve as updates to the last outlook issued prior to the target period. These outlooks, issued daily, are evaluated over a relatively long period of record, 1999–2011, using standard verification measures to assess accuracy; practically perfect forecasts are used to assess skill. Results show a continual increase in the skill of all outlooks during the study period, and increases in the frequency at which these outlooks are skillful on an annual basis.

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Nathan M. Hitchens and Harold E. Brooks

Abstract

The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper represents an initial effort to examine the quality of these forecasts. Convective outlooks are plotted on a latitude–longitude grid with 80-km grid spacing and evaluated using storm reports to calculate verification measures including the probability of detection, frequency of hits, and critical success index. Results show distinct improvements in forecast performance over the duration of the study period, some of which can be attributed to apparent changes in forecasting philosophies.

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Jose A. Algarin Ballesteros and Nathan M. Hitchens

Abstract

During the coldest months of the year, weather systems bring a variety of winter weather to most of the continental United States in the form of snow, sleet, and freezing rain, which along with strong winds, low clouds, and reduced visibilities may create dangerous conditions. These weather conditions can result in major disruptions in air travel, leading to delays and cancellations of hundreds or thousands of flights, thus affecting the plans of millions of travelers. To assess the specific meteorological factors that prompt flight delays and cancellations in the Midwest region of the United States during wintertime, a comprehensive study was performed on nine of the largest airports (by passenger boardings) in the area.

Flight delay and cancellation data from 11 winter seasons (2005–06 to 2015–16) were collected from the Bureau of Transportation Statistics (BTS) and analyzed along with climatological data from the National Centers for Environmental Information (NCEI). A classification scheme was developed, and each flight was categorized according to the meteorological factor that could have prompted its delay. The results of the study revealed that visibility was the main meteorological factor affecting midwestern airports, with low ceilings as a close second. Blizzards were the main cause for flight cancellations.

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Nathan M. Hitchens and Harold E. Brooks

Abstract

Among the Storm Prediction Center’s (SPC) probabilistic convective outlook products are forecasts specifically targeted at significant severe weather: tornadoes that produce EF2 or greater damage, wind gusts of at least 75 mi h−1, and hail with diameters of 2 in. or greater. During the period of 2005–15, for outlooks issued beginning on day 3 and through the final update to the day 1 forecast, the accuracy and skill of these significant severe outlooks are evaluated. To achieve this, criteria for the identification of significant severe weather events were developed, with a focus on determining days for which outlooks were not issued, but should have been based on the goals of the product. Results show that significant tornadoes and hail are generally well identified by outlooks, but significant wind events are underforecast. There exist differences between verification measures when calculating them based on 1) only those days for which outlooks were issued and 2) days with outlooks or missed events; specifically, there were improvements in the frequency of daily skillful forecasts when disregarding missed events. With the greatest number of missed events associated with significant wind events, forecasts for this hazard are identified as an area of future focus for the SPC.

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Nathan M. Hitchens, Michael E. Baldwin, and Robert J. Trapp

Abstract

Extreme precipitation was identified in the midwestern United States using an object-oriented approach applied to the NCEP stage-II hourly precipitation dataset. This approach groups contiguous areas that exceed a user-defined threshold into “objects,” which then allows object attributes to be diagnosed. Those objects with precipitation maxima in the 99th percentile (>55 mm) were considered extreme, and there were 3484 such objects identified in the midwestern United States between 1996 and 2010. Precipitation objects ranged in size from hundreds to over 100 000 km2, and the maximum precipitation within each object varied between 55 and 104 mm. The majority of occurrences of extreme precipitation were in the summer (June, July, and August), and peaked in the afternoon into night (1900–0200 UTC) in the diurnal cycle. Consistent with the previous work by the authors, this study shows that the systems that produce extreme precipitation in the midwestern United States vary widely across the convective-storm spectrum.

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Nathan M. Hitchens, Harold E. Brooks, and Russ S. Schumacher

Abstract

The climatology of heavy rain events from hourly precipitation observations by Brooks and Stensrud is revisited in this study using two high-resolution precipitation datasets that incorporate both gauge observations and radar estimates. Analyses show a seasonal cycle of heavy rain events originating along the Gulf Coast and expanding across the eastern two-thirds of the United States by the summer, comparing well to previous findings. The frequency of extreme events is estimated, and may provide improvements over prior results due to both the increased spatial resolution of these data and improved techniques used in the estimation. The diurnal cycle of heavy rainfall is also examined, showing distinct differences in the strength of the cycle between seasons.

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Nathan M. Hitchens, Harold E. Brooks, and Michael P. Kay

Abstract

A method for determining baselines of skill for the purpose of the verification of rare-event forecasts is described and examples are presented to illustrate the sensitivity to parameter choices. These “practically perfect” forecasts are designed to resemble a forecast that is consistent with that which a forecaster would make given perfect knowledge of the events beforehand. The Storm Prediction Center’s convective outlook slight risk areas are evaluated over the period from 1973 to 2011 using practically perfect forecasts to define the maximum values of the critical success index that a forecaster could reasonably achieve given the constraints of the forecast, as well as the minimum values of the critical success index that are considered the baseline for skillful forecasts. Based on these upper and lower bounds, the relative skill of convective outlook areas shows little to no skill until the mid-1990s, after which this value increases steadily. The annual frequency of skillful daily forecasts continues to increase from the beginning of the period of study, and the annual cycle shows maxima of the frequency of skillful daily forecasts occurring in May and June.

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Patrick T. Marsh, John S. Kain, Valliappa Lakshmanan, Adam J. Clark, Nathan M. Hitchens, and Jill Hardy

Abstract

Convection-allowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convection-allowing models are still unrefined. This paper proposes a method of deriving calibrated probabilistic forecasts of rare events from deterministic forecasts by fitting a parametric kernel density function to the model’s historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts.

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