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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 308 from a north-south orientation; they were found to occur on 33.4% of spring days, and the most favorable position was2100.98 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The hazards attributed to supercell thunderstorms are primarily considered to be tornadoes, large hail, and damaging winds, but flooding from heavy and extreme rainfall is not as often considered with these storms. As a result there has been little research on the role that supercells play in the production of heavy and extreme rainfall events. In order to assess the contribution of supercells to the climatology of short-duration precipitation extremes the present study uses the Warning Decision Support System — Integrated Information to objectively identify supercell thunderstorms from mosaicked radar data during the year 2009 and associate them with high-resolution, accurate multisensor precipitation estimates at time scales of one hour. Supercells are found to be more likely than non-supercells to produce extreme and heavy precipitation, and comparisons are also made between storm types according to month. [Copyright &y& Elsevier]

The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper repre-sents 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research establishes a methodology to quantify the characteristics of convective cloud systems that produce subdiurnal extreme precipitation. Subdiurnal extreme precipitation events are identified by examining hourly precipitation data from 48 rain gauges in the midwestern United States during the period 1956–2005. Time series of precipitation accumulations for 6-h periods are fitted to the generalized Pareto distribution to determine the 10-yr return levels for the stations. An extreme precipitation event is one in which precipitation exceeds the 10-yr return level over a 6-h period. Return levels in the Midwest vary between 54 and 93 mm for 6-h events. Most of the precipitation contributing to these events falls within 1–2 h. Characteristics of the precipitating systems responsible for the extremes are derived from the National Centers for Environmental Prediction stage II and stage IV multisensor precipitation data. The precipitating systems are treated as objects that are identified using an automated procedure. Characteristics considered include object size and the precipitation mean, variance, and maximum within each object. For example, object sizes vary between 96 and 34 480 km2, suggesting that a wide variety of convective precipitating systems can produce subdiurnal extreme precipitation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]