Geostationary Lightning Mapper Research Articles

Lightning Evolution in Hailstorms From the Geostationary Lightning Mapper Over the Contiguous United States

AbstractAs two metrics that depict convection features, lightning and hail are generated through different physical mechanisms but are related. The Geostationary Lightning Mapper (GLM) provides a unique approach to monitoring and studying the continual evolution of lightning in hailstorms. In this study, 3 years of GLM observations were used to analyze the lightning evolution in hailstorms over the contiguous United States. The lightning rate had two peaks at −2 min (t1) (relative to the hail occurrence time) and 59 min (t3), with a valley at 23 min (t2). The lightning rates of large hail were lower at t1 and higher at t2 than those of small hail. The maximum lightning rate grid generally moved from northwest to southeast but suddenly moved westward around 23 min. Observations of WWLLN confirmed these results but also highlighted differences, such as the high ratio between the WWLLN and GLM lightning rates around t1 and t3. The events per flash and flash duration/area/energy showed opposite variations with lightning rate, and their low values were accompanied by high lightning rate and low detection efficiency of GLM relative to WWLLN. However, the event energy continued to decrease with two local maxima at approximately t1 and t3. Furthermore, if the regional event rate was used in the lightning jump (LJ) algorithm, LJs preceded 71% of the hail reports, and the mean lead time reached 29 min. These results provide new insights into lightning features in hailstorms and verify the performance of the GLM in studying and monitoring hailstorms.

Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

Accurate and timely prediction of lightning occurrences plays a crucial role in safeguarding human well-being and the global environment. Machine-learning-based models have been previously employed for nowcasting lightning occurrence, offering advantages in computation efficiency. However, these models have been hindered by limited accuracy due to inadequate representation of the intricate mechanisms driving lightning and a restricted training dataset. To address these limitations, we present a machine learning approach that integrates aerosol features to more effectively capture lightning mechanisms, complemented by enriched satellite observations from the Geostationary Lightning Mapper (GLM). Through training a well-optimized LightGBM model, we successfully generate spatially continuous (0.25° by 0.25°) and hourly lightning nowcasts over the Contiguous United States (CONUS) during the summer season, surpassing the performance of competitive baselines. Model performance is evaluated using various metrics, including accuracy (94.3%), probability of detection (POD, 75.0%), false alarm ratio (FAR, 38.1%), area under curve of precision–recall curve (PRC-AUC, 0.727). In addition to the enriched dataset, the improved performance can be attributed to the inclusion of aerosol features, which has significantly enhanced the model. This crucial aspect has been overlooked in previous studies. Moreover, our model unravels the influence of aerosol composition and loading on lightning formation, indicating that high aerosol loading consisting of sulfates and organic compounds tends to enhance lightning activity, while black carbon inhibits it. These findings align with current scientific knowledge and demonstrate the immense potential for elucidating the complex mechanisms underlying aerosol-associated lightning phenomena.

Making a Superbolt: Reconciling Observations of the Optically Brightest Lightning on Earth From Different Satellites

AbstractWe previously documented geographic distributions of the optically brightest lightning on Earth—known as “superbolts”—using two space‐based instruments: the photodiode detector (PDD) on the Fast On‐orbit Recording of Transient Events (FORTE) satellite and the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites. In this study, we further examine the superbolts identified by the PDD and GLM to reconcile the differences between their geographic distributions. We find that both the physical extent of the parent flash and the development speed of its leaders are important for making a superbolt. The oceanic PDD superbolts tend to occur early in flashes that rapidly expand laterally into long horizontal “megaflashes.” The top GLM superbolts occur over land at later times in particularly large megaflashes. These land‐based flashes grow more slowly until they extend over multiple hundreds of kilometers. The FORTE PDD missed these delayed superbolts due to limitations in its triggering. Coincident Tropical Rainfall Measuring Mission measurements show that the warm season megaflash superbolts detected by Lightning Imaging Sensor/GLM and wintertime oceanic superbolts observed by the PDD occur in otherwise similar thunderstorm environments. Both are marked by: low storm heights (<10 km), widespread precipitation near the surface, small infrared brightness temperature gradients, and low flash rates. We suggest that the vertically compact, stratiform nature of these clouds provides favorable conditions for superbolt production.

Spider Lightning Characterization: Integrating Optical, NLDN, and GLM Detection

Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the discharges were analyzed by merging the video images with lightning flash data from the National Detection Lightning Network (NLDN) and the Geostationary Lightning Mapper (GLM). Spider lightning discharges primarily occurred during the later stages of the overall lightning activity when there was a decrease in the flash count and flash locations were drifting apart. The propagation path of the spider discharge was predominantly luminous and exhibited an extended duration, ranging from 300 ms to 1720 ms, with most of the path remaining continuously illuminated. Occasionally, observed discharges produced cloud-to-ground flashes (CG) along their propagation paths. This study represents the first attempt to utilize video images, NLDN, and GLM data to investigate the correlation between visual observed spider lightning events and detection networks. These combined datasets facilitated the characterization of the observed spider lightning discharges.

Lightning-Based Tropical Cyclone Rapid Intensification Guidance

Abstract With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.

A Survey of Thunderstorms That Produce Megaflashes Across the Americas

AbstractWe previously observed that long‐horizontal lightning flashes exceeding 100 km in length, known as “megaflashes,” occur preferentially in certain thunderstorms. In this study, we develop a cluster feature approach for automatically documenting the evolutions of thunderstorm systems from continuous lightning observations provided by the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites (GOES). We apply this methodology to GOES‐16 GLM observations from 2018 to mid‐2022 to improve our understanding of megaflash‐producing storms. We find that megaflashes occur in long‐lived (median: 14 hr) storms that grow to exceptional sizes (median: 11,984 km2) while they propagate across long distances (median: 622 km) compared to ordinary storms. The first megaflashes are typically produced within 15 min of the storm reaching its peak intensity and extent. However, most megaflashes occur ≥13 hr after the initial megaflash activity, and are sufficiently close to convection to suggest initiation in the thunderstorm core (where GLM has difficulty detecting faint early light sources from megaflashes). Megaflashes generated outside of convection are rare, accounting for 2.7% of the sample using a 50 km convective distance threshold, but also tend to be larger than normal megaflashes, possibly due to having direct access to electrified stratiform clouds through which megaflashes propagate.

Preatmospheric Detection of a Meter-sized Earth Impactor

On 2020 September 18 U.S. Government (USG) sensors detected a bolide with peak bolometric magnitude of −19 over the Western Pacific. The impact was also detected by the Geostationary Lightning Mapper instrument on the GOES-17 satellite and infrasound sensors in Hawaii. The USG measurements reported a steep entry angle of 67° from horizontal from a radiant 13° east of north and an impact speed of 11.7 km s−1. Interpretation of all energy yields produces a preferred energy estimate of 0.4 kt TNT, corresponding to a 23,000 kg, 3 m diameter meteoroid. A postimpact search of telescopic images found that the Asteroid Terrestrial-impact Last Alert System survey captured the object just 10 minutes prior to impact at an Earth-centered distance of nearly 11,900 km with apparent magnitude m = 12.5. The object appears as a 0.44° streak originating on the eastern edge of the image, extending one-third of the USG state-vector-based prediction of 1.26° over the 30 s exposure. The streak shows brightness variability consistent with small asteroid rotation. The position of Earth’s shadow, the object’s size, and its consistency with the reported USG state vector confirm the object is likely natural. This is the eighth preatmospheric detection of a near-Earth asteroid (NEA) impactor and the closest initial telescopic detection prior to impact. The high altitude of peak fireball brightness suggests it was a weak object comparable in many respects with 2008 TC3 (the Almahata Sitta meteorite), with an absolute magnitude H = 32.5 and likely low albedo. Therefore, we suggest the NEA was a C-complex asteroid.

The Urban Lightning Effect Revealed With Geostationary Lightning Mapper Observations

AbstractWithin the Charlotte, North Carolina, to Atlanta, Georgia, megaregion (Charlanta), the Atlanta metropolitan area has been shown to augment proximal cloud‐to‐ground (CG) lightning occurrence. Although numerous studies have documented this “urban lightning effect” (ULE) with regard to CG lightning, relatively few have investigated urban effects on distributions of total lightning (TL). Moreover, there has yet to be a study of the ULE using TL observations from the Geostationary Lightning Mapper (GLM). In an effort to fill this gap, we investigated spatial distributions of TL around the cities of Atlanta, GA, Greenville, SC, and Charlotte, NC, using GLM data collected during the warm seasons of 2018–2021. Analyses reveal augmentation of TL intensity and frequency over the major cities of Atlanta and Charlotte, with a diminished urban signal over the smaller city of Greenville. This work also demonstrated the potential efficacy of the emerging satellite‐based TL climatology in ULE studies.

Parallax Shift in GOES ABI Data

A parallax shift is a displacement in the apparent navigated position of a feature that arises because of its perspective from the viewing platform and is also a function of the feature height. For Geostationary Operational Environmental Satellite (GOES) imagery, this shift is especially apparent away from the satellite subpoint. Users should understand the degree of this shift when combining GOES Advanced Baseline Imager (ABI) imagery with other data, such as radar and lightning. However, it can be challenging, especially at spatial resolutions around the cloud/storm scale. This article explores parallax displacement for both uniform and computed cloud-top heights. Parallax shift will be shown using two case studies. The first case is from 7 September 2021, in which northern Illinois hailstorms are examined using ground-based Level II NEXRAD radar data, GOES-16 ABI imagery, and Geostationary Lightning Mapper data. The second case, on 9 April 2021, examines an eruption of the La Soufrière volcano on St. Vincent from the differing perspectives of GOES16 and -17. The discussion of these cases will show how parallax is an apparent displacement that will vary depending on what satellites are used for observation, where the phenomenon is with respect to the satellite, and the height of the phenomenon being analyzed. Newer satellite instruments with finer spatial resolutions and improved georeferencing will maximize data usability at more extreme angles and require users to account for the accompanying enhanced parallax shift. Even at lesser angles, parallax displacement is an important consideration for many meteorological and other applications.

LNOx Emission Model for Air Quality and Climate Studies Using Satellite Lightning Mapper Observations

AbstractA lightning nitrogen oxides (LNOx) emissions model using satellite‐observed lightning optical energy is introduced for utilization in Air Quality modeling systems. The effort supports assessments of air‐quality/climate coupling as related to the influence of LNOx on atmospheric chemistry. The Geostationary Lightning Mapper (GLM), International Space Station Lightning Imaging Sensor (ISS‐LIS), and the Tropical Rainfall Measuring Mission (TRMM) LIS data are used to examine the efficacy of the method, extend the previously derived LNOx record, and demonstrate a path for using ISS‐LIS observations to cross‐calibrate regional LNOx estimates from the future global constellation of geostationary lightning observations. A detailed evaluation of the GLM data set is provided to establish the robustness of observations for LNOx estimates and to make preliminary assessments of the LNOx emissions model. Seasonal and geographical variation, land/ocean contrast, and annual fluctuation in the GLM observed lightning activity and flash optical energy are provided. GLM detection substantially degrades with the increase in the field of view, resulting in 44% more flashes and 40% less optical energy observation by GLM‐16 (compared to GLM‐17) to the east of the middle‐longitude between the two mappers (106.2°W). Regular horizontal striations are found in the optical energy product. On average, GLM flashes matched to the cloud‐to‐ground flashes have ∼30% longer duration, 50%–70% more extension, and ≥100% higher optical energy compared to the unmatched flashes (assumed to be intra‐cloud). The results from summer‐long chemical transport simulations using LNOx generated from the emission model agrees with previous studies and shows consistency across the GLM/LIS data sets.

Monte Carlo Simulations for Evaluating the Accuracy of Geostationary Lightning Mapper Detection Efficiency and False Alarm Rate Retrievals

Abstract Performance assessments of the Geostationary Lightning Mapper (GLM) are conducted via comparisons with independent observations from both satellite-based sensors and ground-based lightning detection (reference) networks. A key limitation of this evaluation is that the performance of the reference networks is both imperfect and imperfectly known, such that the true performance of GLM can only be estimated. Key GLM performance metrics such as detection efficiency (DE) and false alarm rate (FAR) retrieved through comparison with reference networks are affected by those networks’ own DE, FAR, and spatiotemporal accuracy, as well as the flash matching criteria applied in the analysis. This study presents a Monte Carlo simulation–based inversion technique that is used to quantify how accurately the reference networks can assess GLM performance, as well as suggest the optimal matching criteria for estimating GLM performance. This is accomplished by running simulations that clarify the specific effect of reference network quality (i.e., DE, FAR, spatiotemporal accuracy, and the geographical patterns of these attributes) on the retrieved GLM performance metrics. Baseline reference network statistics are derived from the Earth Networks Global Lightning Network (ENGLN) and the Global Lightning Dataset (GLD360). Geographic simulations indicate that the retrieved GLM DE is underestimated, with absolute errors ranging from 11% to 32%, while the retrieved GLM FAR is overestimated, with absolute errors of approximately 16% to 44%. GLM performance is most severely underestimated in the South Pacific. These results help quantify and bound the actual performance of GLM and the attendant uncertainties when comparing GLM to imperfect reference networks.

Simultaneous Space‐Based Observations of TGFs and Lightning Optical Emission: Characteristics of Lightning

AbstractA previous study of the relative timing between optical lightning emission detected by the Geostationary Lightning Mapper and a sample of 24 simultaneous Terrestrial Gamma‐ray Flashes (TGFs) triggering the Fermi Gamma‐ray Burst Monitor has shown that the gamma‐ray signal appears at the same time as or slightly before the lightning stroke optical signal. A similar result has been demonstrated using simultaneous optical and gamma‐ray data from the Atmosphere‐Space Interactions Monitor. In this work, we add 95 additional untriggered (offline) TGFs to further investigate the correlations between the gamma‐rays and the associated lightning optical emission. Analysis of radio sferics has shown that TGFs are associated with relatively high lightning peak currents. The current results show that TGFs are associated with short duration, relatively low intensity optical signals in low‐to‐moderate flash rate thunderstorm cells, consistent with lightning leaders that produce TGFs occurring some distance below the cloud top and/or expending a fraction of their energy in accelerating relativistic electrons rather than in producing bright optical emission.

A Method for Assimilating Pseudo Dewpoint Temperature as a Function of GLM Flash Extent Density in GSI‐Based EnKF Data Assimilation System—A Proof of Concept Study

AbstractIn this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn‐on‐Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short‐term severe weather forecast is preliminarily assessed in this proof‐of‐concept study. A high‐impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short‐term forecasts. The results show that the assimilation of additional FED‐based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short‐term (3‐hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED‐based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism.

Correction and calibration of atmospheric impact observations in GOES GLM data.

The Earth's atmosphere is impacted daily by both meteoroids and artificial objects. Calibrated observations of the emitted light at sufficiently high sampling rates can enable or improve the estimation of impactor attributes such as size, cohesion, trajectory, and composition, but are difficult to obtain owing to the unpredictability, brevity, and high dynamic (brightness) range of impacts. Ground-based camera systems have successfully monitored small regions of the atmosphere at video frame rates and with limited radiometric capabilities, but most impacts occur over the 70% of the Earth's surface covered by water and are therefore missed by these networks. The Geostationary Lightning Mapper (GLM) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 provide near-hemispherical coverage at 500 frames per second. These data have been shown to contain the signatures of many independently confirmed impacts, often from both viewing angles simultaneously, and constitute an observational resource that is currently unparalleled in the public domain. NASA's Asteroid Threat Assessment Project has implemented an automated impact detection pipeline that processes data from GLM daily. Given a detected impact, the GLM data contain a wealth of information for use in quantitative follow-up analyses. However, impact events differ from lightning in ways that violate key assumptions built into GLM's design. The result is that GLM's onboard processing introduces errors into pixel observations of impact events and the calibrated energies near the periphery of the detector may be substantially overestimated. We present methods for mitigating these and other issues to produce a data product more suitable for impact analyses than the existing GLM lightning product.

Optimization of Narrow-Band Wide-Angle Filtering in a Geostationary Lightning Mapper

Optimization of Narrow-Band Wide-Angle Filtering in a Geostationary Lightning Mapper

О возможности использования данных геостационарного детектора молний для исследования плазменных явлений

The article deals with scientific and technical problems associated with the functionality of the geostationary lightning mapper, which is currently used for meteorological monitoring. Results of the study into the Schumann resonance phenomenon and the technical parameters of the mapper were analyzed simultaneously. A hypothesis is offered which suggests that there are pulsations in the time dependences of the radiation power of lightning activity at frequencies corresponding to Schumann resonance. A new application of the geostationary lightning mapper for studying plasma phenomena is proposed. Adding to the mapper an acousto-optic filter and a camera, which has the functions of switching the resolution/frame rate parameters, is shown to be useful for both meteorological and plasma studies.

Possibility of using GLM data for studying plasma phenomena

The article deals with scientific and technical problems associated with the functionality of the geostationary lightning mapper, which is currently used for meteorological monitoring. Results of the study into the Schumann resonance phenomenon and the technical parameters of the mapper were analyzed simultaneously. A hypothesis is offered which suggests that there are pulsations in the time dependences of the radiation power of lightning activity at frequencies corresponding to Schumann resonance. A new application of the geostationary lightning mapper for studying plasma phenomena is proposed. Adding to the mapper an acousto-optic filter and a camera, which has the functions of switching the resolution/frame rate parameters, is shown to be useful for both meteorological and plasma studies.

Development of New Observation Operators for Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data Using GSI EnKF: Tests with Two Convective Events over the United States

Abstract In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases. Significance Statement Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems.

A Terrestrial Gamma‐Ray Flash From the 2022 Hunga Tonga–Hunga Ha'apai Volcanic Eruption

AbstractThe Hunga Tonga–Hunga Ha'apai submarine volcano recently resumed activity. Violent eruptions on 14th and 15th January 2022 launched a tall ash plume that produced extremely high lightning rates. Here we report a terrestrial gamma‐ray flash (TGF) that was produced by the volcanic lightning and observed from space by the Fermi Gamma‐ray Burst Monitor (GBM). Observations by radio lightning networks and especially by the Geostationary Lightning Mapper show that the only lightning close enough to produce a TGF detectable by Fermi GBM was from the volcano's plume. With the observing duration of Fermi, observing a single TGF is consistent with the hypothesis that the volcanic lightning of this eruption produced TGFs at the average rate of thunderstorm lightning. The observation of a strong TGF from space also indicates that the electric field was oriented so as to accelerate electrons upward.

Relationship Between Lightning, Precipitation, and Environmental Characteristics at Mid‐/High Latitudes From a GLM and GPM Perspective

AbstractThis study applies new satellite datasets and methodologies to build on previous research exploring the physical relationship between lightning and precipitation in mid‐/high latitudes. Specifically, 3 years of Geostationary Lightning Mapper and Global Precipitation Measurement Mission core satellite coincident observations are examined to investigate relationships between lightning flash rate and microwave characteristics of convective precipitation features (cPFs) over the Americas and surrounding oceans between ±50° latitude. Mid‐/high latitude cPFs with lightning are characterized by colder temperatures of maximum 30 dBz echo top height and a smaller range of microwave brightness temperatures when compared to the tropics. Brightness temperature characteristics of electrically active cPFs are highly correlated to radar‐diagnosed ice mass and largely insensitive to synoptic‐scale proxies for convective strength and organization. Low flash density cPFs tend to be more sensitive to synoptic‐scale instability and shear than high flash density cPFs. Regional differences in the environmental forcing and characteristics of electrically active cPFs are shown. For example, the elevated terrain surrounding the Amazon River Basin is characterized by stronger vertical updrafts indicated by higher values of normalized CAPE while the La Plata River Basin is characterized by both stronger updrafts and higher values of radar‐diagnosed ice water mass.