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Matthew R. Kumjian and Wiebke Deierling

ABSTRACT

Lightning flashes during snowstorms occur infrequently compared to warm-season convection. The rarity of such thundersnow events poses an additional hazard because the lightning is unexpected. Because cloud electrification in thundersnow storms leads to relatively few lightning discharges, studying thundersnow events may offer insights into mechanisms for charging and possible thresholds required for lightning discharges. Observations of four northern Colorado thundersnow events that occurred during the 2012/13 winter are presented. Four thundersnow events in one season strongly disagrees with previous climatologies that used surface reports, implying thundersnow may be more common than previously thought. Total lightning information from the Colorado Lightning Mapping Array and data from conterminous United States lightning detection networks are examined to investigate the snowstorms’ electrical properties and to compare them to typical warm-season thunderstorms. Data from polarimetric WSR-88Ds near Denver, Colorado, and Cheyenne, Wyoming, are used to reveal the storms’ microphysical structure and determine operationally relevant signatures related to storm electrification. Most lightning occurred within convective cells containing graupel and pristine ice. However, one flash occurred in a stratiform snowband, apparently triggered by a tower. Depolarization streaks were observed in the radar data prior to the flash, indicating electric fields strong enough to orient pristine ice crystals. Direct comparisons of similar lightning- and nonlightning-producing convective cells reveal that though both cells likely produced graupel, the lightning-producing cell had larger values of specific differential phase and polarimetric radar–derived ice mass. Compared to warm-season thunderstorms, the analyzed thundersnow storms had similar electrical properties but lower flash rates and smaller vertical depths, suggesting they are weaker, ordinary thunderstorms lacking any warm (>0°C) cloud depth.

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Michael Peterson, Chuntao Liu, Douglas Mach, Wiebke Deierling, and Christina Kalb

Abstract

A unique dataset of coincident high-altitude passive microwave and electric field observations taken by the NASA ER-2 aircraft is used to assess the feasibility of estimating electric fields above electrified clouds using ubiquitous global and multidecadal satellite products. Once applied to a global dataset, such a product would provide a unique approach for diagnosing and monitoring the current sources of the global electric circuit (GEC).

In this study an algorithm has been developed that employs ice scattering signals from 37- and 85-GHz passive microwave observations to characterize the electric fields above clouds overflown by the ER-2 aircraft at nearly 20-km altitude. Electric field estimates produced by this passive microwave algorithm are then compared to electric field observations also taken by the aircraft to assess its potential future utility with satellite datasets. The algorithm is shown to estimate observed electric field strengths over intense convective clouds at least 71% (58%) of the time over land and 43% (40%) of the time over the ocean to within a factor of 2 from 85-GHz (37 GHz) passive microwave observations. Electric fields over weaker clouds can be estimated 58% (41%) of the time over land and 22% (8%) of the time over the ocean from 85-GHz (37 GHz) passive microwave observations. The accuracy of these estimates is limited by systematic errors in the observations along with other factors. Despite these sources of error, the algorithm can produce reasonable estimates of electric fields over carefully selected individual electrified clouds that differ from observations by less than 20 V m−1 for clouds that produce 200–400 V m−1 electric fields at 20 km.

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Domingo Muñoz-Esparza, Robert D. Sharman, and Wiebke Deierling

Abstract

We explore the use of machine learning (ML) techniques, namely, regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels [20–45 kft (~6–14 km) in altitude]. In particular, we develop a series of RT-based algorithms that include random forests (RF) and gradient-boosted regression trees (GBRT) methods. Numerical weather prediction model prognostic variables and derived turbulence diagnostics based on 6-h forecasts from the 3-km High-Resolution Rapid Refresh model are used as features to train these data-driven models. Training and evaluation are based on turbulence estimates of eddy dissipation rate (EDR) obtained from automated in situ aircraft reports. Our baseline RF model, consisting of 100 trees with 30 layers of maximum depth, significantly reduces forecast errors for EDR < 0.1 m2/3 s−1 (which corresponds roughly to null and light turbulence) when compared with a simple regression model, increasing the probability of detection and in turn reducing the number of false alarms. Model complexity reduction via GBRT and feature-relevance analyses is performed, indicating that considerable execution speedups can be achieved while maintaining the model’s predictive skill. Overall, the ML models exhibit enhanced performance in discriminating the EDR forecast among the light, moderate, and severe turbulence categories. In addition, these artificial intelligence techniques significantly simplify the generation of new NWP and grid-spacing specific turbulence forecast products.

Open access
Michael Peterson, Wiebke Deierling, Chuntao Liu, Douglas Mach, and Christina Kalb

Abstract

High-altitude atmospheric electricity measurements have been used to calculate the conduction (Wilson) currents that are supplied to the global electric circuit (GEC) by individual electrified clouds. Quantifying the global average current and assessing its temporal variability is a challenge, however, because it requires measurements in every stormy region of the world. Thus, a retrieval algorithm has been developed to infer the electric fields and Wilson currents above electrified weather from NASA ER-2 passive microwave high-altitude aircraft observations that are also common satellite products.

This study documents the adaptation of the passive microwave electric field and the Wilson current retrieval algorithm for use with satellite platforms. Three distinct variants on the algorithm are produced to respond to specific use cases that differ in 1) whether swath or microwave feature data are available to describe the lateral extent of electrified clouds, 2) the availability of coincident radar data to characterize the vertical structure of electrified clouds, and 3) the prioritization of scientific accuracy or computational expense and product latency. The Wilson currents produced by the satellite retrievals are compared with each other and also with coincident lightning measurements and the Carnegie curve. The advantages, caveats, and limitations of each variant are discussed.

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Evan A. Kalina, Katja Friedrich, Brian C. Motta, Wiebke Deierling, Geoffrey T. Stano, and Nezette N. Rydell

Abstract

Synoptic weather, S-band dual-polarization radar, and total lightning observations are analyzed from four thunderstorms that produced “plowable” hail accumulations of 15–60 cm in localized areas of the Colorado Front Range. Results indicate that moist, relatively slow (5–15 m s−1) southwesterly-to-westerly flow at 500 hPa and postfrontal low-level upslope flow, with 2-m dewpoint temperatures of 11°–19°C at 1200 LST, were present on each plowable hail day. This pattern resulted in column-integrated precipitable water values that were 132%–184% of the monthly means and freezing-level heights that were 100–700 m higher than average. Radar data indicate that between one and three maxima in reflectivity Z (68–75 dBZ) and 50-dBZ echo-top height (11–15 km MSL) occurred over the lifetime of each hailstorm. These maxima, which imply an enhancement in updraft strength, resulted in increased graupel and hail production and accumulating hail at the surface within 30 min of the highest echo tops. The hail core had Z ~ 70 dBZ, differential reflectivity Z DR from 0 to −4 dB, and correlation coefficient ρ HV of 0.80–0.95. Time–height plots reveal that these minima in Z DR and ρ HV gradually descended to the surface after originating at heights of 6–10 km MSL ~15–60 min prior to accumulating hailfall. Hail accumulations estimated from the radar data pinpoint the times and locations of plowable hail, with depths greater than 5 cm collocated with the plowable hail reports. Three of the four hail events were accompanied by lightning flash rates near the maximum observed thus far within the thunderstorm.

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Katelyn A. Barber, Wiebke Deierling, Gretchen Mullendore, Cathy Kessinger, Robert Sharman, and Domingo Muñoz-Esparza

Abstract

Convectively induced turbulence (CIT) is an aviation hazard that continues to be a forecasting challenge as operational forecast models are too coarse to resolve turbulence affecting aircraft. In particular, little is known about tropical maritime CIT. In this study, a numerical simulation of a tropical oceanic CIT case where severe turbulence was encountered by a commercial aircraft is performed. The Richardson number (Ri), subgrid-scale eddy dissipation rate (EDR), and second-order structure functions (SF) are used as diagnostics to determine which may be used for CIT related to developing and mature convection. Model-derived subgrid-scale EDR in past studies of midlatitude continental CIT was shown to be a good diagnostic of turbulence but underpredicted turbulence intensity and areal coverage in this tropical simulation. SF diagnosed turbulence with moderate to severe intensity near convection and agreed most with observations. Further, SF were used to diagnose turbulence for developing convection. Results show that the areal coverage of turbulence associated with developing convection is less than mature convection. However, the intensity of turbulence in the vicinity of developing convection is greater than the turbulence intensity in the vicinity of mature convection highlighting developing convection as an additional concern to aviation.

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Blake J. Allen, Edward R. Mansell, David C. Dowell, and Wiebke Deierling

Abstract

Total lightning observations that will be available from the GOES-R Geostationary Lightning Mapper (GLM) have the potential to be useful in the initialization of convection-resolving numerical weather models, particularly in areas where other types of convective-scale observations are sparse or nonexistent. This study used the ensemble Kalman filter (EnKF) to assimilate real-data pseudo-GLM flash extent density (FED) observations at convection-resolving scale for a nonsevere multicell storm case (6 June 2000) and a tornadic supercell case (8 May 2003).

For each case, pseudo-GLM FED observations were generated from ground-based lightning mapping array data with a spacing approximately equal to the nadir pixel width of the GLM, and tests were done to examine different FED observation operators and the utility of temporally averaging observations to smooth rapid variations in flash rates.

The best results were obtained when assimilating 1-min temporal resolution data using any of three observation operators that utilized graupel mass or graupel volume. Each of these three observation operators performed well for both the weak, disorganized convection of the multicell case and the much more intense convection of the supercell case.

An observation operator using the noninductive charging rate performed poorly compared to the graupel mass and graupel volume operators, a result that appears likely to be due to the inability of the noninductive charging rate to account for advection of space charge after charge separation occurs.

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Robinson Wallace, Katja Friedrich, Wiebke Deierling, Evan A. Kalina, and Paul Schlatter

Abstract

Thunderstorms that produce hail accumulations at the surface can impact residents by obstructing roadways, closing airports, and causing localized flooding from hail-clogged drainages. These storms have recently gained an increased interest within the scientific community. However, differences that are observable in real time between these storms and storms that produce nonimpactful hail accumulations have yet to be documented. Similarly, the characteristics within a single storm that are useful to quantify or predict hail accumulations are not fully understood. This study uses lightning and dual-polarization radar data to characterize hail accumulations from three storms that occurred on the same day along the Colorado–Wyoming Front Range. Each storm’s characteristics are verified against radar-derived hail accumulation maps and in situ observations. The storms differed in maximum accumulation, either producing 22 cm, 7 cm, or no accumulation. The magnitude of surface hail accumulations is found to be dependent on a combination of in-cloud hail production, storm translation speed, and hailstone melting. The optimal combination for substantial hail accumulations is enhanced in-cloud hail production, slow storm speed, and limited hailstone melting. However, during periods of similar in-cloud hail production, lesser accumulations are derived when storm speed and/or hailstone melting, identified by radar presentation, is sufficiently large. These results will aid forecasters in identifying when hail accumulations are occurring in real time.

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Katja Friedrich, Robinson Wallace, Bernard Meier, Nezette Rydell, Wiebke Deierling, Evan Kalina, Brian Motta, Paul Schlatter, Thomas Schlatter, and Nolan Doesken

Abstract

In recent years, hail accumulations from thunderstorms have occurred frequently enough to catch the attention of the National Weather Service, the general public, and news agencies. Despite the extreme nature of these thunderstorms, no mechanism is currently in place to obtain adequate reports, measurements, or forecasts of accumulated hail depth. To better identify and forecast hail accumulations, the Colorado Hail Accumulation from Thunderstorms (CHAT) project was initiated in 2016 with the goals of collecting improved and more frequent hail depth reports on the ground as well as studying characteristics of storms that produce hail accumulations in Colorado. A desired outcome of this research is to identify predictors for hail-producing thunderstorms typically occurring along the Colorado Front Range that might be used as operational nowcast products in the future. During the 2016 convective season, we asked amateur meteorologists to send general information, photos, and videos on hail depth using social media. They submitted over 58 reports in Colorado with information on location, time, depth, and areal coverage of hail accumulations. We have analyzed dual-polarization radar and lightning mapping array data from 32 thunderstorms in Colorado, which produced between 0.5 and 50 cm of hail accumulation on the ground, to identify characteristics unique to storms with hail accumulations. This preliminary analysis shows how enhanced in-cloud hail presence and surface accumulation can be tracked throughout the lifetime of a thunderstorm using dual-polarization radar and lightning data, and how hail accumulation events are associated with large in-cloud ice water content, long hailfall duration, or a combination of these.

Open access
Timothy J. Lang, Eldo E. Ávila, Richard J. Blakeslee, Jeff Burchfield, Matthew Wingo, Phillip M. Bitzer, Lawrence D. Carey, Wiebke Deierling, Steven J. Goodman, Bruno Lisboa Medina, Gregory Melo, and Rodolfo G. Pereyra

Abstract

During November 2018–April 2019, an 11-station very high frequency (VHF) Lightning Mapping Array (LMA) was deployed to Córdoba Province, Argentina. The purpose of the LMA was validation of the Geostationary Lightning Mapper (GLM), but the deployment was coordinated with two field campaigns. The LMA observed 2.9 million flashes (≥ five sources) during 163 days, and level-1 (VHF locations), level-2 (flashes classified), and level-3 (gridded products) datasets have been made public. The network’s performance allows scientifically useful analysis within 100 km when at least seven stations were active. Careful analysis beyond 100 km is also possible. The LMA dataset includes many examples of intense storms with extremely high flash rates (>1 s−1), electrical discharges in overshooting tops (OTs), as well as anomalously charged thunderstorms with low-altitude lightning. The modal flash altitude was 10 km, but many flashes occurred at very high altitude (15–20 km). There were also anomalous and stratiform flashes near 5–7 km in altitude. Most flashes were small (<50 km2 area). Comparisons with GLM on 14 and 20 December 2018 indicated that GLM most successfully detected larger flashes (i.e., more than 100 VHF sources), with detection efficiency (DE) up to 90%. However, GLM DE was reduced for flashes that were smaller or that occurred lower in the cloud (e.g., near 6-km altitude). GLM DE also was reduced during a period of OT electrical discharges. Overall, GLM DE was a strong function of thunderstorm evolution and the dominant characteristics of the lightning it produced.

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