Balloon-borne electric field meters have been used to study the electrical environment of thunderstorms since the 1970s; even so, descriptions of how they function are incomplete. This study serves as a consolidated and comprehensive reference to how these instruments work, including physical and electrical descriptions of the instrument and details about how they are launched into thunderstorms. The mathematical method used to convert the modulated voltage signal recorded by the electric field meter into a three-dimensional measurement of the electric field is also provided here for the first time. The study also expands on these methods by examining how a swinging instrument package can affect the measured signals and the demodulated electric field. Balloon-borne electric field measurements are frequently used to estimate the charge density in thunderstorms using a one-dimensional Gauss approximation. An analysis of simulated balloon flights through simulated storms is used to evaluate how representative this approximation is. In general, charge retrievals were representative but underestimated the charge density at the balloon package. Better agreement was found with a horizontal average of the thundercloud charge density, but the estimation was still prone to errors in the vicinity of some lightning flashes. SIGNIFICANCE STATEMENT: This study serves as a consolidated and comprehensive reference on how balloon-borne electric field meters function. This includes a physical and electrical description of the instrument, details of how the instrument is launched into thunderstorms, and the mathematical methods required to convert the recorded signals into measurements of electric field. In addition, the effects of a swinging instrument were investigated, and methods to identify swing in the recorded data are documented. Finally, a simulation was performed to test the ability of balloon-borne electric field meters to estimate the charge density in realistic thunderstorm environments.

In cumulus clouds, aerosol concentrations control cloud droplet concentrations, modifying cloud radiative properties, precipitation processes, and cloud electrification. However, mechanisms of aerosol-deep convection interactions are not well understood due to complex cloud dynamics and microphysics. We investigate the interaction of aerosols with isolated deep convection using Large Eddy Simulations of two cases during the TRacking Aerosol Convection interactions ExpeRiment (TRACER) near Houston, Texas, using a joint cell-thermal tracking algorithm. Cumulus thermals are droplet generators, since supersaturation and droplet nucleation coincide with thermal centers, where the strongest updrafts occur. Primary ice crystal formation does not take place inside thermals, but at layers where previous thermals detrained moisture. As subsequent thermals containing supercooled droplets penetrate these layers, hail and graupel form at or near these thermals. Higher aerosol concentrations result in higher droplet concentrations that suppress drizzle, delay warm rain processes, and transport more moisture aloft. This increases snow and ice amount, as well as graupel and hail, leading to more lightning. Polluted thermals initiate at slightly higher altitudes, and are slightly larger and faster, suggesting a weak invigoration. We also find more thermals per cell, but fewer isolated cells, since convection is more aggregated and intense, especially near the end of the 24 h simulation. Non-linear mesoscale feedback likely triggered by temperature and moisture responses to aerosol-thermal interactions causes the aggregation. Time-lagged aerosol-reinitialization experiments show that the mesoscale response is the predominant forcing for the invigoration. These changes happen within one day, on a smaller scale than previously suggested.

Abstract Climate change has caused highly uncertain precipitation regimes and associated hydrometeorological hazards over global drylands. Accurate precipitation measurements are vital for monitoring dryland ecohydrology. Satellite precipitation products (SPPs), including the Integrated Multi‐satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG‐V07) and the Global Satellite Mapping of Precipitation (GSMaP‐V08), provide quasi‐global coverage but require regional‐scale ground validation to assess errors and reliability, over drylands. We evaluated IMERG‐V07 and GSMaP‐V08 using measurements from 107 West Texas Mesonet (WTM) sites from 2018 to 2022. Precipitation over the semi‐arid WTM domain is concentrated during the May–October wet season. The observed southeast–to‐northwest precipitation gradient (i.e., higher to lower) exists primarily due to dryline‐induced convergence and orographic lifting. At monthly scale, IMERG (GSMaP) overestimates (underestimates), with GSMaP demonstrating greater overall accuracy. SPPs perform best during the peak precipitation months (May, September, and October) and reproduce interannual fluctuations well. The GSMaP performs better on monthly and annual timescales. The mean bootstrap 95% confidence interval for correlation coefficient were [0.759, 0.825] and [0.837, 0.892] for IMERG and GSMaP, respectively. Monthly accumulations of SPPs exhibit intensity dependent biases in high‐intensity precipitation regimes. For near‐real‐time (NRT) detection of extreme precipitation, IMERG‐Early shows higher sensitivity via higher detection but also more false alarms. The GSMaP‐NRT provides balanced event detection, with lower false alarms and higher critical success index in most cases. However, the GSMaP reliability‐flag and IMERG quality‐index were also found to be decisive factors. Results highlight the need for intensity‐ and timescale‐sensitive validation to improve SPP algorithms for advanced precipitation monitoring and forecasting over drylands.

Convective clouds play an important role in Earth's climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled "Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)." ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region's mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle. SIGNIFICANCE STATEMENT: The ESCAPE field experiment provided unique observations of coastal convective cloud vertical motions, microphysics, and precipitation across a wide range of summertime aerosol and meteorological regimes. The highest aerosol concentrations occurred near the refineries in eastern Houston but did not contribute to the cloud condensation nuclei and ice-nucleating particles. The airborne measurements included frequent sampling of intense convective updraft dynamics and microphysics. A novel radar-based sampling of convective cells provided unique observations of their 3D structure throughout their life cycle. Mobile trucks equipped with soundings provided a detailed sampling of the sea-breeze structure and evolution. These datasets will be used for improving high-resolution simulations of high-impact events in coastal urbanized areas.

Real-time measurements of lightning locations can improve flight safety by providing aircraft operators with valuable information about nearby weather conditions. Lightning warnings can be especially valuable when piloting aircraft that are more susceptible to a direct strike such as electric aircraft, hydrogen-powered aircraft, and even UAVs with composite skins. At best, weather updates are broadcast from weather services every 2.5 to 5 mins, but it's not uncommon for an intermittent connection to cause service stability issues. Therefore, an aircraft-mounted lightning mapper might be the most practical source of real-time lightning information for pilots. This work investigates the in-flight performance of the aircraft-mounted Stormscope Weather Mapping System (WX-500 Series 2) through comparisons to the Houston Lightning Mapping Array, National Lightning Detection Network, and the GOES - Geostationary Lightning Mapper. Measurements from two thunderstorms near Houston, TX, yielded WX-500 detection efficiencies of 33 % and 42 % for intracloud flashes, 75 % and 64 % for cloud to ground flashes, and 53 % and 79 % for total flashes. The WX-500 bearing measurement was accurate to within +14 degrees (sigma), which improved to +4 degrees when integration time was increased from 2 to 30 s and clear outliers were ignored. The WX-500 range measurement was overestimated by an average of +74 km (+50 km) when the average true flash distance was 94 km. The WX-500 accurately depicted the boundary of lightning activity at an integration time of 1 min which is sufficient for the circumnavigation of thunderstorms.

AbstractPrevious studies of lightning detection by radar mostly consisted of observations with reflector‐antenna systems yielding slow volume scan times. Phased array radars offer much faster scan times that are likely to capture echoes from propagating lightning channels. Rapidly updated range‐height indicator scans were used to observe severe storms that occurred in central Oklahoma with the fully digital S‐band Horus PAR to examine echoes from lightning plasma. Numerous lightning echoes were observed during the sampling period in good spatial and temporal agreement with lightning mapping array detections of very high frequency radiation sources. Statistically, they result in increased horizontal reflectivity factor, deviations in radial velocity and spectrum width, highly variable differential reflectivity and differential phase, and decreases in correlation coefficient. Results presented also highlight the capability of phased array radars to better observe lightning compared to current radars, and aid in the study of storm electrification and lightning physics.

Understanding cloud-top microphysics is essential for improving weather forecasting and convection monitoring. In this study, we propose a simplified cloud scattering light model to analyze the influence of inhomogeneities in the cloud microphysical properties on the observations of blue corona discharges (BLUEs) from Atmosphere-Space Interactions Monitor. The results show that the depth inferred from the inhomogeneous model is consistently lower than that from the homogeneous model, with the largest difference reaching 2 km. We then present a new approach to inferring inhomogeneous cloud microphysical profiles based on the optical signals from BLUEs using radio-inferred source depths. These profiles match well with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation lidar measurements and show a transition nearby tropopause with different exponential change rates above and below it. Our study highlights the potential of combining optical and radio observations of BLUEs to rapidly assess cloud microphysics and monitor convective activity.

The Experiment of Sea-Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field project deployed two aircraft and ground-based assets in the vicinity of Houston, Texas, between 27 May and 2 July 2022, examining how meteorological conditions, dynamics, and aerosols control the initiation, early growth stage, and evolution of coastal convective clouds. To ensure that airborne- and ground-based assets were deployed appropriately, a forecasting and nowcasting team was formed. Daily forecasts guided real-time decision-making by assessing synoptic weather conditions, environmental aerosol, and a variety of atmospheric modeling data to assign a probability for meeting specific ESCAPE campaign objectives. During the research flights, a small team of forecasters provided "nowcasting" support by analyzing radar, satellite, and new model data in real time. The nowcasting team proved invaluable to the campaign operation, as sometimes changing environmental conditions affected, for example, the timing of convective initiation. In addition to the success of the forecasting and nowcasting teams, the ESCAPE campaign offered a unique "testbed" opportunity where in-person and virtual support both contributed to campaign objectives. The forecasting and nowcasting teams were each composed of new and experienced forecasters alike, where new forecasters were given invaluable experience that would otherwise be difficult to attain. Both teams received training on forecast models, map analysis, Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), and thermodynamic sounding analysis before the beginning of the campaign. In this article, the ESCAPE forecasting and nowcasting teams reflect on these experiences, providing potentially useful advice for future field campaigns requiring forecasting and nowcasting support in a hybrid virtual/in-person framework.

Shifts in precipitation patterns due to global warming highlight the need to understand precipitation variability across diverse climatic regimes. Research on precipitation distribution, patterns, intensity, duration, and frequency over drylands is lacking but crucial for quantitative precipitation estimation and climate projection accuracy. Drylands depend solely on satellite remote sensing due to unavailability of continuous ground-based precipitation monitoring. Validating satellite-based precipitation observations is crucial to ensure accuracy before their use in weather prediction and climate models. In exploring biases in satellite retrievals for regional-scale variability over semi-arid Southwest US (West Texas), we performed comprehensive intercomparison between high spatially resolved ground-based observations (West Texas Mesonet) and satellite derived precipitation estimates (GPM-IMERG). Analyses revealed noticeable spatial gradients in precipitation from west-east and south-north. Satellite retrievals exhibited a positive relative bias of precipitation indicating overestimation, with 62.6% (37.4%) measurements showing a positive (a negative) bias, and 2.6% exhibiting extreme negative bias.

Eighteen lightning flash rate parameterization schemes (FRPSs) were investigated in a Weather Research and Forecasting model coupled with chemistry cloud-resolved simulation of the 29-30 May 2012 supercell storm system observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. Most of the observed storm's meteorological conditions were well represented when the model simulation included both convective damping and lightning data assimilation techniques. Newly-developed FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The schemes are based on relationships between lightning and various kinematic, structural, and microphysical thunderstorm characteristics (e.g., cloud top height, hydrometeors, reflectivity, and vertical velocity) available in the model. The results suggest the model-simulated graupel and snow/ice hydrometeors require scaling factors to more closely represent proxy observations. The model-simulated lightning flash trends and total flashes generated by each scheme over the simulation period are compared with observations from the central Oklahoma Lightning Mapping Array. For this supercell system, 13 of the 18 schemes overpredicted flashes by >100% with the group of FRPSs based on storm kinematics and structure (particularly updraft volume) performing slightly better than the hydrometeor-based schemes. During the storm's first 4 hr, the upward cloud ice flux FRPS, which is based on the combination of vertical velocity and hydrometeors, well represents the observed total flashes and flash rate trend; while, the updraft volume scheme well represents the observed flash rate peak and subsequent sharp decline in flash rate.

Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.

A cloud‐resolved storm and chemistry simulation of a severe convective system in Oklahoma constrained by anvil aircraft observations of NOx was used to estimate the mean production of NOx per flash in this storm. An upward ice flux scheme was used to parameterize flash rates in the model. Model lightning was also constrained by observed lightning flash types and the altitude distribution of flash channel segments. The best estimate of mean NOx production by lightning in this storm was 80–110 mol per flash, which is smaller than many other literature estimates. This result is likely due to the storm having been a high flash rate event in which flash extents were relatively small. Over the evolution of this storm a moderate negative correlation was found between the total flash rate and flash extent and energy per flash. A longer‐term simulation at 36‐km horizontal resolution with parameterized convection was used to simulate the downwind transport and chemistry of the anvil outflow from the same storm. Convective transport of low‐ozone air from the boundary layer decreased ozone in the anvil outflow by up to 20–40 ppbv compared with the initial conditions, which contained stratospheric influence. Photochemical ozone production in the lightning‐NOx enhanced convective plume proceeded at a rate of 10–11 ppbv per day in the 9–11 km outflow layer over the 24‐hr period of downwind transport to the Southern Appalachians. Photochemical production plays a large role in the restoration of upper tropospheric ozone following deep convection.

Properties of 7488 thunderstorms are summarized for June-September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) fi eld campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument's measurements to a data- defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning fl ash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

The National Science Foundation-sponsored Lake-Effect Electrification (LEE) field campaign intensive observation periods occurred between November and early February 2022-23 across the eastern Lake Ontario region. Project LEE documented, for the first time, the total lightning and electrical charge structures of lake-effect storms and the associated storm environment using a lightning mapping array (LMA), a mobile dual-polarization X-band radar, and balloon-based soundings that measured vertical profiles of temperature, humidity, wind, electric field, and hydrometeor types. LEE also observed abundant wind turbine-initiated lightning, which is climatologically more likely during the winter. The frequent occurrence of intense lake-effect storms and the proximity of a wind farm with nearly 300 turbines each more than 100 m tall to the lee of Lake Ontario provided an ideal laboratory for this study. The field project involved many undergraduate (>20) and graduate students. Some foreseen and unforeseen challenges included clearing the LMA solar panels of snow and continuous operation in low-sunlight conditions, large sonde balloons prematurely popping due to extremely cold conditions, sonde line breaking, recovering probes in deep snow in heavily forested areas, vehicles getting stuck in the snowpack, and an abnormally dry season for parts of the LEE domain. In spite of these difficulties, a dataset was collected in multiple lake-effect snowstorms (11 observation periods) and one extratropical cyclone snowstorm that clarifies the electrical structure of these systems. A key finding was the existence of a near-surface substantial positive charge layer (1 nC m-3) near the shoreline during lake-effect thunderstorms.

The dual-polarization radar characteristics of severe storms are commonly used as indicators to estimate the size and intensity of deep convective updrafts. In this study, we track rapid fl uctuations in updraft intensity and size by objectively identifying polarimetric fi ngerprints such as Z DR and K DP columns, which serve as proxies for mixed-phase updraft strength. We quantify the volume of Z DR and K DP columns to evaluate their utility in diagnosing temporal variability in lightning fl ash characteristics. Specifically, fi cally, we analyze three severe storms that developed in environments with low-to- moderate instability and strong 0-6-km wind shear in northern Alabama during the 2016-17 VORTEX-Southeast fi eld campaign. In these three cases (a tornadic supercell embedded in stratiform precipitation, a nontornadic supercell, and a supercell embedded within a quasi-linear convective system), we fi nd that the volume of the K DP columns exhibits a stronger correlation with the total fl ash rate. The higher covariability of the K DP column volume with the total fl ash rate suggests that the overall electrification fi cation and precipitation microphysics were dominated by cold cloud processes. The lower covariability with the Z DR column volume indicates the presence of nonsteady updrafts or a less prominent role of warm rain processes in graupel growth and subsequent electrification. fi cation. Furthermore, we observe that the majority of cloud-to-ground (CG) lightning strikes a carried negative charge to the ground. In contrast to fi ndings from a tornadic supercell over the Great Plains, lightning fl ash initiations in the Alabama storms primarily occurred outside the footprint of the Z DR and K DP column objects.

There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed.

Our observational contribution to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tracking Aerosol Convection Interactions Experiment (TRACER) was the deployment of additional Lightning Mapping Array sensors to provide enhanced capability to the Houston Lightning Mapping Array (HLMA) during the TRACER intensive operational period. To that end, the Texas Tech University personnel (Professor Bruning and Dr. Brunner, and graduate students Jessica Souza, David Singewald, Stephanie Weiss, and Matthew Miller) deployed two portable LMA antennae at locations G and B shown in the map below. The map also shows the predicted lightning flash detection efficiency in black contours, as well as color-shaded very-high-frequency (VHF) source detection efficiency, which roughly corresponds to the sensitivity to lightning channel detail. Feeds from these sensors were integrated in the HLMA processing in real-time and in post-processing, partially leveraging National Science Foundation support through the TRACER campaign that supported the core HLMA, operated by Timothy Logan.