Regional, Seasonal, and Diurnal Variations of Cloud-to-Ground Lightning with Large Impulse Charge Moment Changes

2014 ◽  
Vol 142 (10) ◽  
pp. 3666-3682 ◽  
Author(s):  
Nick K. Beavis ◽  
Timothy J. Lang ◽  
Steven A. Rutledge ◽  
Walter A. Lyons ◽  
Steven A. Cummer
Keyword(s):  
United States ◽  
Atlantic Ocean ◽  
Great Plains ◽  
Southeastern United States ◽  
Northern Great Plains ◽  
Total Charge ◽  
Central Plains ◽  
Convective Systems ◽  
Seasonal And Diurnal Variations ◽  
Mesoscale Convective

Abstract The use of both total charge moment change (CMC) and impulse charge moment change (iCMC) magnitudes to assess the potential of a cloud-to-ground (CG) lightning stroke to induce a mesospheric sprite has been well described in the literature, particularly on a case study basis. In this climatological study, large iCMC discharges for thresholds of >100 and >300 C km in both positive and negative polarities are analyzed on a seasonal basis. Also presented are local solar time diurnal distributions in eight different regions covering the lower 48 states as well as the adjacent Atlantic Ocean, including the Gulf Stream. The seasonal maps show the predisposition of large positive iCMCs to dominate across the northern Great Plains, with large negative iCMCs favored in the southeastern United States year-round. During summer, the highest frequency of large positive iCMCs across the upper Midwest aligns closely with the preferred tracks of nocturnal mesoscale convective systems (MCSs). As iCMC values increase above 300 C km, the maximum shifts eastward of the 100 C km maximum in the central plains. Diurnal distributions in the eight regions support these conclusions, with a nocturnal peak in large iCMC discharges in the northern Great Plains and Great Lakes, an early to midafternoon peak in the Intermountain West and the southeastern United States, and a morning peak in large iCMC discharge activity over the Atlantic Ocean. Large negative iCMCs peak earlier in time than large positive iCMCs, which may be attributed to the growth of large stratiform charge reservoirs following initial convective development.

scholarly journals Assessment of the High-Resolution Rapid Refresh Model’s Ability to Predict Mesoscale Convective Systems Using Object-Based Evaluation

2015 ◽  
Vol 30 (4) ◽  
pp. 892-913 ◽  
Author(s):  
James O. Pinto ◽  
Joseph A. Grim ◽  
Matthias Steiner
Keyword(s):  
United States ◽  
High Resolution ◽  
Size Distribution ◽  
Great Plains ◽  
Southeastern United States ◽  
Mesoscale Convective Systems ◽  
Eastern United States ◽  
Convective Systems ◽  
Object Based ◽  
Mesoscale Convective

Abstract An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model’s skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time–dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model’s tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity.

scholarly journals A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking

2018 ◽  
Vol 57 (7) ◽  
pp. 1599-1621 ◽  
Author(s):  
Alex M. Haberlie ◽  
Walker S. Ashley
Keyword(s):  
United States ◽  
Machine Learning ◽  
Great Plains ◽  
Radar Reflectivity ◽  
Machine Learning Algorithms ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Mesoscale Convective ◽  
Minimization Procedure ◽  
Optimal Approach

AbstractThis research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.

scholarly journals Severe Convective Storms across Europe and the United States. Part I: Climatology of Lightning, Large Hail, Severe Wind, and Tornadoes

2020 ◽  
Vol 33 (23) ◽  
pp. 10239-10261 ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Pieter Groenemeijer ◽  
Roger Edwards ◽  
Harold E. Brooks ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
The United States ◽  
Maximum Activity ◽  
Severe Weather ◽  
Eastern United States ◽  
Convective Storms ◽  
Convective Systems ◽  
Lightning Detection ◽  
Mesoscale Convective

AbstractAs lightning-detection records lengthen and the efficiency of severe weather reporting increases, more accurate climatologies of convective hazards can be constructed. In this study we aggregate flashes from the National Lightning Detection Network (NLDN) and Arrival Time Difference long-range lightning detection network (ATDnet) with severe weather reports from the European Severe Weather Database (ESWD) and Storm Prediction Center (SPC) Storm Data on a common grid of 0.25° and 1-h steps. Each year approximately 75–200 thunderstorm hours occur over the southwestern, central, and eastern United States, with a peak over Florida (200–250 h). The activity over the majority of Europe ranges from 15 to 100 h, with peaks over Italy and mountains (Pyrenees, Alps, Carpathians, Dinaric Alps; 100–150 h). The highest convective activity over continental Europe occurs during summer and over the Mediterranean during autumn. The United States peak for tornadoes and large hail reports is in spring, preceding the maximum of lightning and severe wind reports by 1–2 months. Convective hazards occur typically in the late afternoon, with the exception of the Midwest and Great Plains, where mesoscale convective systems shift the peak lightning threat to the night. The severe wind threat is delayed by 1–2 h compared to hail and tornadoes. The fraction of nocturnal lightning over land ranges from 15% to 30% with the lowest values observed over Florida and mountains (~10%). Wintertime lightning shares the highest fraction of severe weather. Compared to Europe, extreme events are considerably more frequent over the United States, with maximum activity over the Great Plains. However, the threat over Europe should not be underestimated, as severe weather outbreaks with damaging winds, very large hail, and significant tornadoes occasionally occur over densely populated areas.

scholarly journals Physiologic Specialization of Puccinia triticina on Wheat in the United States in 2013

Plant Disease ◽  
2015 ◽  
Vol 99 (9) ◽  
pp. 1261-1267 ◽  
Author(s):  
J. A. Kolmer ◽  
M. E. Hughes
Keyword(s):  
United States ◽  
Winter Wheat ◽  
Leaf Rust ◽  
Great Plains ◽  
Puccinia Triticina ◽  
The United States ◽  
Northern Great Plains ◽  
Ohio River ◽  
Ohio Valley ◽  
Red Winter Wheat

Collections of Puccinia triticina were obtained from rust-infected leaves provided by cooperators throughout the United States and from wheat fields and breeding plots by USDA-ARS personnel and cooperators in the Great Plains, Ohio River Valley, and southeastern states in order to determine the virulence of the wheat leaf rust population in 2013. Single uredinial isolates (490 total) were derived from the collections and tested for virulence phenotype on 20 lines of Thatcher wheat that are near-isogenic for leaf rust resistance genes. In 2013, 79 virulence phenotypes were described in the United States. Virulence phenotypes MBTNB, TNBGJ, and MCTNB were the three most common phenotypes. Phenotypes MBTNB and MCTNB are both virulent to Lr11, and MCTNB is virulent to Lr26. MBTNB and MCTNB were most common in the soft red winter wheat region of the southeastern states and Ohio Valley. Phenotype TNBGJ is virulent to Lr39/41 and was widely distributed throughout the hard red winter wheat region of the Great Plains. Isolates with virulence to Lr11, Lr18, and Lr26 were common in the southeastern states and Ohio Valley region. Isolates with virulence to Lr21, Lr24, and Lr39/41 were frequent in the hard red wheat region of the southern and northern Great Plains.

Characteristics and Predictability of Midwestern United States Drought

2021 ◽  
Author(s):  
Andrew Hoell ◽  
Trent W. Ford ◽  
Molly Woloszyn ◽  
Jason A. Otkin ◽  
Jon Eischeid
Keyword(s):  
United States ◽  
Great Plains ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Northern Great Plains ◽  
Ohio Valley ◽  
Midwestern United States ◽  
Wave Trains

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

scholarly journals Near-Storm Environments of Outbreak and Isolated Tornadoes

2018 ◽  
Vol 33 (5) ◽  
pp. 1397-1412 ◽  
Author(s):  
Alexandra K. Anderson-Frey ◽  
Yvette P. Richardson ◽  
Andrew R. Dean ◽  
Richard L. Thompson ◽  
Bryan T. Smith
Keyword(s):  
United States ◽  
Great Plains ◽  
Southeastern United States ◽  
Probability Of Detection ◽  
Geographical Region ◽  
Self Organizing Maps ◽  
Specific Shape ◽  
The Difference ◽  
Tornado Warning ◽  
Tornado Outbreaks

Abstract Between 2003 and 2015, there were 5343 outbreak tornadoes and 9389 isolated tornadoes reported in the continental United States. Here, the near-storm environmental parameter-space distributions of these two categories are compared via kernel density estimation, and the seasonal, diurnal, and geographical features of near-storm environments of these two sets of events are compared via self-organizing maps (SOMs). Outbreak tornadoes in a given geographical region tend to be characterized by greater 0–1-km storm-relative helicity and 0–6-km vector shear magnitude than isolated tornadoes in the same geographical region and also have considerably higher tornado warning-based probability of detection (POD) than isolated tornadoes. A SOM of isolated tornadoes highlights that isolated tornadoes with higher POD also tend to feature higher values of the significant tornado parameter (STP), regardless of the specific shape of the area of STP. For a SOM of outbreak tornadoes, when two outbreak environments with similarly high magnitudes but different patterns of STP are compared, the difference is primarily geographical, with one environment dominated by Great Plains and Midwest outbreaks and another dominated by outbreaks in the southeastern United States. Two specific tornado outbreaks are featured, and the events are placed into their climatological context with more nuance than typical single proximity sounding-based approaches would allow.

scholarly journals Assimilation of Satellite-Derived Soil Moisture for Improved Forecasts of the Great Plains Low-Level Jet

2020 ◽  
Vol 148 (11) ◽  
pp. 4607-4627
Author(s):  
Craig R. Ferguson ◽  
Shubhi Agrawal ◽  
Mark C. Beauharnois ◽  
Geng Xia ◽  
D. Alex Burrows ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Wind Speed ◽  
Data Assimilation ◽  
Great Plains ◽  
Wrf Model ◽  
Added Value ◽  
Low Level ◽  
Convective Systems ◽  
South Central ◽  
Mesoscale Convective

AbstractIn the context of forecasting societally impactful Great Plains low-level jets (GPLLJs), the potential added value of satellite soil moisture (SM) data assimilation (DA) is high. GPLLJs are both sensitive to regional soil moisture gradients and frequent drivers of severe weather, including mesoscale convective systems. An untested hypothesis is that SM DA is more effective in forecasts of weakly synoptically forced, or uncoupled GPLLJs, than in forecasts of cyclone-induced coupled GPLLJs. Using the NASA Unified Weather Research and Forecasting (NU-WRF) Model, 75 GPLLJs are simulated at 9-km resolution both with and without NASA Soil Moisture Active Passive SM DA. Differences in modeled SM, surface sensible (SH) and latent heat (LH) fluxes, 2-m temperature (T2), 2-m humidity (Q2), PBL height (PBLH), and 850-hPa wind speed (W850) are quantified for individual jets and jet-type event subsets over the south-central Great Plains, as well as separately for each GPLLJ sector (entrance, core, and exit). At the GPLLJ core, DA-related changes of up to 5.4 kg m−2 in SM can result in T2, Q2, LH, SH, PBLH, and W850 differences of 0.68°C, 0.71 g kg−2, 59.9 W m−2, 52.4 W m−2, 240 m, and 4 m s−1, respectively. W850 differences focus along the jet axis and tend to increase from south to north. Jet-type differences are most evident at the GPLLJ exit where DA increases and decreases W850 in uncoupled and coupled GPLLJs, respectively. Data assimilation marginally reduces negative wind speed bias for all jets, but the correction is greater for uncoupled GPLLJs, as hypothesized.

scholarly journals A Unified Effort to Fight an Enemy of Wheat and Barley: Fusarium Head Blight

Plant Disease ◽  
2012 ◽  
Vol 96 (12) ◽  
pp. 1712-1728 ◽  
Author(s):  
Marcia McMullen ◽  
Gary Bergstrom ◽  
Erick De Wolf ◽  
Ruth Dill-Macky ◽  
Don Hershman ◽  
...  
Keyword(s):  
United States ◽  
Fusarium Head Blight ◽  
Great Plains ◽  
Plant Disease ◽  
Management Strategies ◽  
The United States ◽  
Export Market ◽  
Northern Great Plains ◽  
Head Blight ◽  
Food And Feed

Wheat and barley are critical food and feed crops around the world. Wheat is grown on more land area worldwide than any other crop. In the United States, production of wheat and barley contributes to domestic food and feed use, and contributes to the export market and balance of trade. Fifteen years ago, Plant Disease published a feature article titled “Scab of wheat and barley: A re-emerging disease of devastating impact”. That article described the series of severe Fusarium head blight (FHB) epidemics that occurred in the United States and Canada, primarily from 1991 through 1996, with emphasis on the unparalleled economic and sociological impacts caused by the 1993 FHB epidemic in spring grains in the Northern Great Plains region. Earlier publications had dealt with the scope and damage caused by this disease in the United States, Canada, Europe, and China. Reviews published after 1997 further described this disease and its impact on North American grain production in the 1990s. This article reviews the disease and documents the information on U.S. FHB epidemics since 1997. The primary goal of this article is to summarize a sustained, coordinated, and collaborative research program that was put in place shortly after the 1993 epidemic, a program intended to quickly lead to improved management strategies and outreach implementation. This program serves as a model to deal with other emerging plant disease threats.

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