Secondary ice production (SIP) remains a major uncertainty in understanding deep convection because its rates and controlling conditions are poorly constrained. Using the Tracking Aerosol Convection Interactions Experiment (TRACER) C-band and operational S-band radar observations, Houston Lightning Mapping Array (HLMA) lightning data, and large-eddy simulations (LESs), this study investigates negative K DP observed above the melting layer as a potential signature of SIP and storm electrification. Among 413 observed deep convective cells, 238 cases (~57.6%) exhibit negative K DP above the melting layer, and 49 of 123 lightning-active cases (~39.8%) show negative K DP near HLMA sources. These observations suggest that negative K DP is associated with small, vertically oriented, electrically aligned ice particles. LESs with spectral bin microphysics reproduce negative K DP with high likelihood only when SIP is included, especially fragmentation of freezing drops, which increases ice water path, decreases liquid water path, and reduces accumulated precipitation by ~7.6% in the examined case.

Abstract Deep convective cells significantly influence Earth’s energy balance and water cycle. However, their accurate representation in numerical models remains challenging due to their small spatiotemporal scales and limited observational constraints. This study examines over ∼400 deep convective cells near Houston, observed by a dual-polarization C-band radar during the Tracking Aerosol Convection Interactions Experiment (TRACER) intensive observation period (June–September 2022). Cells are categorized by lifetime into short-lived (<40 min), intermediate-lived (40–80 min), and long-lived (80+ min) groups. Long-lived cells were broader (∼13.2 km at 2–4-km height) and deeper (∼11.4 km) than short-lived cells (∼6.4-km width, ∼7.31-km height). Using random forest (RF) modeling and correlation analyses, precipitable water vapor (PWV), 2–6-km lapse rate, 0–8-km bulk shear, and fine aerosol mass concentration (Mass_f) are identified as key predictors of cell lifetime. Higher PWV is associated with significantly longer convective cell lifetimes compared to the low-PWV group, particularly within low 2–6-km temperature lapse rate (LR_26km), moderate-to-higher 0–8-km bulk shear (BS_08km), and low-to-moderate Mass_f environments. RF analysis also identifies low-level (0–2 km) equivalent potential temperature, PWV, Mass_f, and surface latent heat flux as key predictors for cell width and height. Short-lived cells have higher aerosol number concentrations (500–1000-nm size range), linked to onshore wind conditions and marine aerosols; however, their low concentration suggests the sensitivity may reflect associated meteorological regimes rather than a direct aerosol effect. Long-lived cells have higher concentrations of organic and sulfate aerosols, while short-lived cells exhibit higher black carbon concentrations. These results highlight the intricate dependence of convective cell lifetimes and structure on environmental moisture, thermodynamics, wind shear, and aerosol characteristics. Significance Statement Accurately representing deep convective cells in numerical models remains challenging due to their small spatial scales and complex interactions with aerosols. This study examines over ∼400 deep convective cells near Houston using C-band radar and other collocated instruments from the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign to investigate how meteorological and aerosol conditions influence cell lifetimes. Results indicate a strong dependence of convective cell lifetime on moisture, 2–6-km temperature lapse rate, 0–8-km wind shear, and fine aerosol mass concentration (Mass_f), with longer-lived cells being deeper and wider. Moisture-induced increases in lifetime are evident across both low and moderate Mass_f conditions, with higher precipitable water vapor (PWV) consistently associated with longer-lived cells. Long-lived cells have higher concentrations of organic and sulfate aerosols, while short-lived cells exhibit higher black carbon concentrations. This work highlights the influence of both meteorological conditions and aerosol properties on convective cell properties.

Abstract. Coastal urban environments exhibit strong vertical and horizontal heterogeneity in aerosol properties, complicating process-level understanding of aerosol–cloud interactions. This study analyzes tethered balloon system (TBS) measurements from 149 flights during summer over the greater Houston, Texas, region as part of the DOE Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection interactions ExpeRiment (TRACER) campaign. We characterize the vertical structure of aerosol number concentrations, size distributions, and inferred cloud condensation nuclei (CCN) concentrations. Air mass history was classified using back-trajectory analysis and k-means clustering into three clusters: (1) marine-influenced, (2) mixed marine and urban emissions, and (3) urban/anthropogenic and long-range transported aerosols. CCN concentrations are estimated from observed size distributions using κ-Köhler theory. The resulting profiles show pronounced vertical variability across clusters, strongly modulated by boundary-layer depth and coastal circulations, leading to substantial variability in the aerosol population available for cloud activation. The marine cluster showed the lowest concentrations, with CCN at 0.8 % supersaturation below 1,000 cm⁻³, while urban and mixed clusters displayed higher concentrations and more complex layering. Profiles influenced by the mixed marine–urban cluster frequently exhibit decoupling between near-surface aerosol and elevated layers, including enhanced accumulation-mode number aloft, consistent with prior TBS-based compositional studies. A September 6–7, 2022 case study demonstrates that mesoscale transport can simultaneously transform both the thermodynamic environment and aerosol population, highlighting the importance of constraining boundary-layer dynamics and airmass origin before attributing cloud changes to aerosol effects in complex coastal environments.

The Sea Breeze Circulation (SBC) influences atmospheric processes at multiple scales in coastal regions. Understanding how SBCs impact the aerosol number budget and aerosol-cloud interaction processes is essential. This study investigates sea breeze-aerosol interactions (SAIs) during 46 summertime SBC events using data from the TRacking Aerosol Convection Interactions Experiment (TRACER) field campaign across urban (main) and rural (supplemental) coastal sites in southern Texas. Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations complement observations to explore spatio-temporal meteorological controls on boundary layer aerosols. During the summertime, Sea Breeze Fronts (SBF) penetrating inland transported cool, moist air over the land, introducing air masses with distinct properties compared to the preexisting continental air. These SAIs cause variability in number concentrations of up to a factor of two, with events typically lasting similar to 5 h before returning to background conditions. SAI impact on aerosols varies with site proximity to water and the preceding sea breeze (SB) history, primarily affecting the marine-influenced accumulation mode. The main site, influenced by both Galveston Bay and the Gulf of Mexico, reflects a stronger marine influence. In contrast, a supplemental site, at a similar shoreline distance but exposed only to the Gulf of Mexico and typically upstream of the urban core, samples SB air that has traversed land and partially regained continental characteristics. Simulations show that the regional SAIs extend similar to 50 km inland and reach up to the boundary layer height. SAIs further decrease cloud condensation nuclei relevant aerosol number concentrations in similar to 20 % of events during SBF passage.

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.

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.

A closed bay‐breeze circulation (BBC) followed by a gulf‐breeze front (GBF) was observed on 10 September 2022 during the Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) TRACER campaign. Using high‐resolution X‐band Scanning Cloud Radar (XSACR) and a newly developed orienteering tape recorder diagram, the study analyzed radar reflectivity and Doppler velocity to identify anomalies and track the evolution of these circulations. The BBC, a mesoscale system approximately 30‐km long, 30‐km wide, and 2‐km deep, formed from enhanced horizontal convective rolls along the Galveston Bay coast, progressing northwestward under a 6 m s −1 onshore flow anomaly and reaching 1.5 km depth with return flow aloft. The inland penetration speed was 2 m s −1 , driven by an observed 6–9 K land‐water temperature contrast. The GBF, coupled to the BBC, intensified with additional southerly flow, penetrating further inland after the BBC collapsed. Passing over the TRACER field site, both fronts significantly impacted boundary layer thermodynamics, dynamics, and aerosol concentration. The BBC event exhibited four lifecycle stages—formation, development, maturation, and dissipation—driven by solar heating, wind field rotation, and interactions with convective eddies and the GBF. This study provides insights into the inland evolution of coastal breeze circulations and their interactions with environmental processes.

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. This study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the Tracking Aerosol Convection Interactions Experiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) field campaigns, machine learning techniques, and parcel model simulations with a superdroplet microphysics scheme. A random forest algorithm identifies in situ vertical velocity w, temperature T, and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D-max < 50 mu m (N-c,N-<50), 50 mu m <= D-max <= 3000 mu m (N-c,N-50-3000), and droplet effective diameter D-e. The results show that LWC, N-c,N-<50, and N-c,N-50-3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D-e, and N-c,N-50-3000 increase, while N-c,N-<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N-c,N-50-3000, and smaller N-c,N-<50, while polluted environments produce greater N-c,N-<50. Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N-c,N-50-3000. Polluted conditions result in a greater N-c,N-<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

We characterize convective clouds associated with sea-breeze circulations (SBC) using multi-agency observations and multi-case ensemble model simulations. The focus is on assessing convective cloud lifecycle properties and their merging behavior, as well as the environmental conditions they are embedded in, particularly SBC features. In total, 46 SBC days over the Houston-Galveston region are selected and simulated using the Weather Research and Forecasting (WRF) model at a gray zone scale with a forecast-like parameterization setup. Advanced techniques, including change-point detection, a Lagrangian cloud tracking method, and a newly developed cell merging and splitting detection algorithm, are applied and/or developed for this study. Our findings indicate that the WRF model at 1 km grid spacing well represents the thermodynamic conditions over the region, as well as SBC timing and intensity. However, for the associated convective cells, WRF overestimates the 30-dBZ echo top height, cell area, and maximum radar reflectivity compared to radar observations. This overestimation is potentially due to under-resolved entrainment processes, an overestimated merging frequency, and the overestimation of updraft intensity. Furthermore, the model exhibits a deficiency in simulating congestus clouds, showing a more rapid transition from shallow to deep convection compared to observed behavior. Moreover, observations indicate stronger, deeper, and wider clouds when merging happens. Conversely, in simulations, the merging process does not necessarily lead to higher or longer-lived cells, as many cases experience rapid and frequent merging and splitting which may result in more variance in convective updraft velocity during the convection lifetime. Plain Language Summary In our study, we used advanced model simulations and real-world observations to understand how sea breezes and thunderstorms develop in the Houston-Galveston area. We focused on 46 specific days when sea breezes were active. Our research aimed to answer questions about how well the model represents the timing and intensity of sea breezes, as well as the life cycle of thunderstorms that form along them. We found that the WRF model accurately predicted when and how intense sea breezes would occur. When looking at the thunderstorms themselves, the model often showed higher cloud tops and larger rainfall areas compared to real observations. It also did not always capture the transition from smaller, shallow clouds to larger, deeper thunderstorms as accurately as we hoped. In addition, in the simulations, the merging of clouds does not typically result in taller or longer-lasting cells. This is because more frequent merging and splitting lead to more variability in upward air motion during the lifetime of cloud.

This study investigates marine and continental stratocumulus (Sc) cloud properties obtained from an automated implementation of a multispectral photometer retrieval. Photometer methods simultaneously retrieve cloud optical depth (τ) and cloud droplet effective radius (re), with estimates for liquid water path (LWP) calculated on the availability of those quantities. These applied methods evaluate retrieved cloud properties for Sc identified during a recent 6 year period over the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program sites in Oklahoma, USA (SGP) and in the Azores, Portugal (ENA). Modest agreement in key quantity retrievals is found between the routine photometer products and multisensor collocated profiling references. Cumulative breakdowns contingent on cloud thickness indicate increases in all retrieved quantities in thicker clouds, with larger discrepancies in the relative performance between the retrievals collected in the presence of drizzle. Under continental cloud conditions, the clouds of a similar thickness and re to those sampled under marine conditions report a factor of 1.5 larger τ and LWP. An r2≅0.65 is found between photometer τ retrievals and shadowband radiometer measurements, with photometer retrievals reporting a high (relative) bias. The τ intercomparisons indicate that variability between retrievals is a factor of three larger than errors reported from individual retrieval input perturbation tests. Photometer re retrievals suggest a low r2 (< 0.1) having a standard deviation ≅ 3 µm when compared to ARM baseline multi-sensor radar/radiometer references (accounting for offsets in the cloud droplet number concentration assumptions of the latter). However, photometer LWP calculations remain relatively unbiased in non-drizzling conditions, with errors O (50 g m−2) and r2≅0.5 to collocated radiometer and interferometer references. Additional sensitivity tests for island influences on marine Sc properties suggest that while island-influenced winds may promote larger cloud LWP or thickness, the influence could be within retrieval method uncertainty and/or collocated instrument variability.

A relative calibration technique has been developed for the US Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) user facility Ka-band ARM Zenith Radars (KAZRs). This method uses the signal attenuation caused by water on the radome to estimate reflectivity factor (Ze) offsets. The wet-radome attenuation (WRA) is assumed to follow a log-linear relationship with rainfall rate during light and moderate rain, as measured by a collocated surface disdrometer. The technique has an uncertainty of approximately 3 dB, due to factors such as disdrometer measurement error, rain variability between radar and disdrometer sample volumes, and the fitting function's uncertainty for the WRA behavior. A practical advantage of this WRA-based approach to shorter-wavelength radar monitoring is that, while it requires a reference disdrometer, it proves feasible for a wider range of collocated disdrometer measurements compared to traditional direct disdrometer comparison at the onset of light rain. This technique thus offers a cost-effective monitoring tool for remote or long-term radar deployments. This calibration technique was applied during the ARM Tracking Aerosol Convection Interactions Experiment (TRACER) from October 2021 through September 2022. The estimated Ze offsets were compared against traditional radar calibration and monitoring methods using available datasets from this campaign. Results show that the WRA-based offsets align closely with mean offsets found between cloud radars and from direct disdrometer comparison near the onset of rain, while also reflecting similar offset and campaign-long trends when compared to collocated, independently calibrated radar wind profilers. Nevertheless, overall, the KAZR Ze offsets estimated during TRACER remained stable at approximately 2 dB lower than the disdrometer estimates from the campaign start until the end of June 2022; afterward, the offsets increased to around 7 dB by the campaign's end. This increase is linked to a drop of about 1 dB in transmitter power toward the end of the project.

Long-term observations of deep convective cloud (DCC) vertical velocity and mass flux were collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon2014/5) experiment. Precipitation echoes from a surveillance weather radar near Manaus, Brazil, are tracked to identify and evaluate the isolated DCC lifecycle evolution during the dry and wet seasons. A radar wind profiler (RWP) provides precipitation and air motion profiles to estimate the vertical velocity, mass flux, and mass transport rates within overpassing DCC cores as a function of the tracked cell lifecycle stage. The average radar reflectivity factor (Z), DCC area (A), and surface rainfall rate (R) increased with DCC lifetime as convective cells were developing, reached a peak as the cells matured, and decreased thereafter as cells dissipated. As the convective cells mature, cumulative DCC properties exhibit stronger updraft behaviors with higher upward mass flux and transport rates above the melting layer (compared with initial and later lifecycle stages). In comparison, developing DCCs have the lowest Z associated with weak updrafts, as well as negative mass flux and transport rates above the melting layer. Over the DCC lifetime, the height of the maximum downward mass flux decreased, whereas the height of the maximum net mass flux increased. During the dry season, the tracked DCCs had higher Z, propagation speed, and DCC area, and were more isolated spatially compared with the wet season. Dry season DCCs exhibit higher Z, mass flux, and mass transport rate while developing, whereas wet season DCCs exhibit higher Z, mass flux, and mass transport rates at later stages.

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. In this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

This study delves into the characteristics of convective clouds induced by Gulf-Breeze Circulations (GBCs) and Bay-Breeze Circulations (BBCs) in the Houston-Galveston region. By using a machine learning method, we find that anticyclonic synoptic patterns prevalent during the summer months significantly contribute to the formation and development of GBC/BBC-induced convective clouds, constituting 71% of cases. Leveraging data from the TRacking Aerosol Convection interactions ExpeRiment (TRACER), we discover that the main site at LaPorte, TX experiences the influence of both GBC and BBC, with frontal passages primarily occurring around 1300 LT. These frontal passages trigger boundary layer updrafts, fostering isolated convective cores characterized by short durition (63 min) and slow movement (5 m/s), particularly within 20-40 km from the coast, and maturing around 80 km inland. In addition to observations, we simulate these convective cases using the WRF model, demonstrating good agreement with TRACER data. Various urban schemes are then explored to probe the impact of the urban heat island effect on the evolution of GBC/BBC structures and subsequent convective cloud development.

This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston-Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June-September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s(-1)), primarily within 20-40 km from the coast.

The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.