Convective Systems Research Articles

Magnetic field effects on the thermal performance of Fe3O4 nanofluids in a forced convection system

This study experimentally investigates the effects of magnetic field strength, Reynolds number, and nanoparticle concentration on the unsteady heat transfer characteristics of a Fe3O4-water nanofluid. The nanofluid was prepared by dispersing nanoparticles in water at concentrations of 0 %, 0.08% v/v and 0.16% v/v, using a stabilizer to maintain dispersion. A double-pipe system, encased in a copper coil (solenoid), was employed to facilitate heat exchange between tap water and the nanofluid under magnetic field. The solenoid exerts the desired magnetic field. The Taguchi method was utilized for experimental design and analysis. Results indicate that the Reynolds number, magnetic field strength, and nanofluid concentration all enhance the heat transfer rate; however, their effects differ due to distinct mechanisms of action. Analysis of variance reveals that the Reynolds number has the most significant impact although at low Reynolds only. This is while the effects of nanoparticle concentration and magnetic field strength are comparable. The findings suggest that even a magnetic field aligned with the fluid flow can serve as an auxiliary factor to enhance heat transfer in ferrofluids, particularly when increasing nanoparticle concentration is constrained by rising pressure drops and the risk of agglomeration.

Contrasting Response of Mesoscale Convective Systems Occurrence Over Tropical Land and Ocean to Increased Sea Surface Temperature

AbstractMesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm‐resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land‐ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation.

Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

Abstract Ensembles of convection-allowing model (CAM) forecasts are increasingly being used in operational numerical weather forecasting. Several approaches have been devised to find consensus among ensemble forecast fields, including the arithmetic ensemble mean and, more recently, the patchwise localized probability-matched (LPM) mean. However, differences in spatial distribution and intensity of precipitation features among ensemble members make it difficult to construct an ensemble mean product that characterizes the consensus while preserving precipitation structures forecasted by the individual ensemble members. To overcome this problem, this study aims to develop and test a method for improving ensemble consensus precipitation forecasts by directly considering the spatial offsets among ensemble members. This study uses a multiscale spatial alignment technique to align the precipitation features of each ensemble member to a common location, and the spatial aligned mean (SAM) is obtained by averaging the realigned members. It is shown that implementing SAM and subsequently applying the LPM technique to the average of all aligned members (SAM–LPM) can significantly improve the warm season precipitation forecast scores using common metrics such as equitable threat score (ETS). Also, improvement in the structure of features of heavy rainfall is shown from summer 2023 flash-flooding cases. Thus, SAM and SAM–LPM can be excellent candidate methods for calculating an ensemble consensus and providing ensemble consensus guidance to forecasters. Significance Statement High-impact rainfall events, such as flash floods, result in many billion-dollar loss events in the United States each year. This study seeks to improve the prediction of such events when using guidance from convection-allowing model (CAM) ensemble forecasts, such as the U.S. operational High-Resolution Ensemble Forecast (HREF) and the nascent Rapid Refresh Forecast System (RRFS). The proposed method, the spatial aligned mean (SAM), directly addresses the common issue of disparity in the predicted location of convective systems among ensemble members that confounds traditional ensemble consensus methods. In this study, it is found that SAM improves ensemble consensus guidance for high-impact rainfall events in both the HREF and the Center for Analysis and Prediction of Storms (CAPS) Finite-Volume Cubed-Sphere (FV3)-limited area model (LAM) CAM ensemble forecast system, a proxy for the future RRFS.

Simulation of a Mesoscale Convective System Using a New Formulation for Advection in a Bin Microphysics: Sensitivity to Advection, Microphysics, and Grid Spacing

Abstract A computationally efficient semi-Lagrangian advection (SLA) scheme designed for microphysical variable advection was implemented into the Weather Research and Forecasting (WRF) Model with spectral bin microphysics (SBM). The primary goal was to reduce the CPU time for advection of the scalar SBM variables and demonstrate its applicability to simulation of a real-data case study in the eta vertical coordinate system. A mesoscale convective system (MCS) in Midlatitude Continental Convective Clouds Experiment (MC3E-0520) was selected for simulations. We compared the SLA and high-order, nonlinear monotonic advection schemes and tested the sensitivity of the simulated radar reflectivity, microphysical, and dynamic properties of the MCS to the choice of microphysical schemes, aerosol concentration, and grid spacing. Simulations using the SLA and monotonic advection schemes were similar, and the differences between them were much smaller than those between the SBM and bulk microphysical schemes tested. Improvement of the grid resolution had a larger impact on the vertical velocity field than did the choice of aerosol concentration. The total computational time in simulations with SLA was about 25% shorter than that with monotonic advection, which resulted from a reduction of more than 50% in computational time required for advection of the microphysical variables. Significance Statement Clouds comprise particles of various sizes and types: aerosols, liquid droplets, ice crystals, hail, and snow. Two major approaches describe the time and spatial evolution of these particle species: solving basic microphysical (bin microphysics) and simplified parameterization (bulk microphysics) equations. Bin microphysics is time-consuming as it operates with size distributions described by multiple mass bins. Among the microphysical processes, the advection of the microphysical variables requires significant CPU time. We tested a new numerical advection scheme that requires several times less computer time than the standard high-order nonlinear schemes being of similar accuracy. Due to the introduced change, the advection of droplet size distributions became less of a factor affecting the total computer time of bin-type schemes. This is an important step toward making the bin approach computationally efficient without losing accuracy. It is shown that the sensitivity of the results to spatial grid spacing and to the choice of microphysical schemes is significant and may be larger than that to variations in the aerosol concentration.

Unveiling the Urban Impact on Mesoscale Convective System Rainfall in the Pearl River Delta Urban Agglomeration Under Typical Synoptic Backgrounds

AbstractMesoscale convective systems (MCSs), the primary drivers of extreme rainfall over the Pearl River Delta (PRD) urban agglomeration, are strongly influenced by synoptic circulations and local geographical environments, including water bodies and topography. However, the urban impact on MCS rainfall under various synoptic backgrounds remain inadequately understood. Using a 20‐year high‐resolution MCS tracking database and self‐organizing map clustering, three typical backgrounds for MCSs, namely weak monsoon‐like, strong monsoon‐like, and low‐pressure system (Types‐1 to 3), impacting the PRD are identified. These backgrounds exhibit pronounced disparities in MCS tracks and temporal variations as well as rainfall distributions. Urban heat island (UHI) significantly alters the spatial patterns under Types‐1 and 2. Specifically, under weak UHI condition, MCS rainfall typically occurs offshore in the morning and shifts inland in the afternoon driven by the land‐sea breeze. However, UHI modifies the low‐level thermal structure, leading to anomalous convergence and instability, which causes morning rainfall to concentrate near coastal cities, while afternoon rainfall expands further inland to the northern rural region. Additionally, the strong southwesterly winds associated with Type‐2 enhance the interaction between topography and urban impact, resulting in even higher rainfall anomalies (+28.9%) over the northeastern region. The findings highlight the crucial role of urban impact and their synergistic effect with synoptic backgrounds and other land surface processes on MCSs.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

How Porosity Influences the Heterogeneous Ice Nucleation Ability of Secondary Organic Aerosol Particles

AbstractDuring processing in deep convective cloud systems, highly viscous or glassy secondary organic aerosol (SOA) particles can develop a porous structure through a process known as atmospheric freeze‐drying. This structural modification may enhance their heterogeneous ice nucleation ability under cirrus conditions through the pore condensation and freezing mechanism. Pristine, compact SOA particles, on the other hand, are recommended to be treated as ice‐inactive in models. This recommendation also applies to internally mixed particles, where a coating layer of secondary organic matter (SOM) deactivates the intrinsic ice nucleation ability of the core, which may be a mineral dust grain. Ice cloud‐processing may also improve the ice nucleation ability of such a composite particle by inducing structural changes in the coating layer, which can release active sites on the mineral surface. In this work, we investigated the change in the ice nucleation ability of pure SOA particles from the ozonolysis of α‐pinene and two types of internally mixed particles (zeolite and coal fly ash particles coated with SOM) after being subjected to the atmospheric freeze‐drying process simulated in an expansion cloud chamber. For pure α‐pinene SOA, we found only a slight improvement in the ice nucleation ability of the ice cloud‐processed, porous particles compared to their pristine, compact counterparts at 221 and 217 K. In contrast, the zeolite and coal fly ash particles, which were initially deactivated by the organic coating, became significantly more ice‐active after atmospheric freeze‐drying, emphasizing that such composite particles cannot be excluded from model simulations of heterogeneous ice formation.

Examining Tropical Convection Features at Storm‐Resolving Scales Over the Maritime Continent Region

AbstractGlobal Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.

Madden Julian Oscillation Moves Faster as the Meridional Moisture Gradient Intensifies in a Warming World

AbstractThe eastward‐moving large‐scale convective system associated with the Madden‐Julian Oscillation (MJO) significantly impact global weather and climate. Recent decades have seen notable changes in the MJO's lifecycle due to non‐uniform tropical ocean warming, with the roles of natural climate variability and anthropogenic influence still requiring quantification. This study examines observed and projected long‐term changes in the MJO phase speed using four twentieth‐century reanalyses and CMIP6 simulations. We find a substantial increase in MJO phase speed in three reanalyses during the twentieth century (0.6–1.2 m s⁻1 century⁻1) and further increase in MJO phase speed during the twenty‐first century (0.3–1.5 m s⁻1 century⁻1), with notable multidecadal fluctuations. We attribute the overall acceleration of the MJO to the global warming‐driven increase in the meridional moisture gradient around the warm pool while attributing the multidecadal variability in the MJO phase speed to changes in the zonal moisture gradient associated with the Pacific Decadal Oscillation.

The Impact of Circular Holes in Twisted Tape Inserts on Forced Convection Heat Transfer

This study investigated heat transfer and friction characteristics in a forced convection system using wavy twisted tape inserts with circular holes. The inserts, with twist ratios (TR) of 8.5, 7.5, and 6.5, were placed inside a test pipe to create turbulent flow. The tapes measured 700 mm in length and 18 mm in width, while the test pipe had an outer diameter of 35 mm and an inner diameter of 30 mm, with a test section length of 700 mm. Airflow rates were adjusted to achieve different bulk mean temperatures. Experimental data were used to develop new correlations for the Nusselt number and friction factor. The Reynolds number (Re) ranged from 4,000 to 14,000. Comparisons between the wavy twisted tape inserts with varying twist ratios and pitches and a smooth tube showed that the highest heat transfer rate was achieved with a twist ratio of 6.5.

The BKM criterion to the 3D double-diffusive magneto convection systems involving planar components

In this paper, we investigate the BKM type blowup criterion applied to 3D double-diffusive magneto convection systems. Specifically, we demonstrate that a unique local strong solution does not experience blow-up at time T, given that ). To prove this, we employ the logarithmic Sobolev inequality in the Besov spaces with negative indices and a well-known commutator estimate established by Kato and Ponce. This result is the further improvement and extension of the previous works by O (2021) and Wu (2023).

Deep Learning‐Based Precipitation Simulation for Tropical Cyclones, Mesoscale Convective Systems, and Atmospheric Rivers in East Asia

AbstractDifferent types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), significantly impact precipitation patterns in East Asia. This study pioneers the application of deep learning (DL) methods, including convolutional neural network, U‐Net, and Attention U‐Net models, to simulate precipitation associated with these weather events. The spatial permutation method is also used to identify key meteorological variables for accurately generating precipitation in DL models. The DL models trained on all timeslots consistently surpass the performance of state‐of‐the‐art numerical simulations, although their efficacy slightly diminishes during extreme weather events. This outperformance is attributed to the appropriate emphasis on key variables that capture precipitation processes, such as low‐level moisture and mid‐level pressure fields. However, new DL models trained separately for TCs, MCSs, and ARs using clipped precipitation as the output does not exceed the performance of the previous DL models. Among all input features, moisture variables contribute the most to precipitation at low intensity, while the importance of other variables increases for more intense precipitation, although some discrepancies vary across models and event types. The spatial results further reveal the detailed locations of variables that are essential for accurately simulating precipitation related to weather events, such as areas of high specific humidity and strong winds. DL models could also acquire useful information from region remote to the events to improve the simulation. Overall, DL models serve as promising tools for simulating and enhancing our understanding of precipitation patterns associated with various weather events in East Asia.

Initiation of a Supercell by Convectively Generated Gravity Waves in the Simulated Kaiyuan Tornadic Event of 3 July 2019

Abstract An EF4-rated supercell tornado occurred on 3 July 2019 in Kaiyuan, China, causing heavy casualties. A three-level nested-grid high-resolution numerical simulation is used to investigate the initiation of the tornadic supercell. Automatic weather station (AWS) data, FY-4A visible satellite data, and Doppler radar data are used to verify the model simulation. The most important aspects of the simulated pre-supercell mesoscale convective system (MCS) and the initiation of the supercell agree with observations. Detailed investigation of the model results reveals that the initial cells form first above a convective boundary layer (CBL) on the dry side of a surface dryline. Above the CBL is a moist layer in terms of relative humidity and the layer is stable. Convectively generated gravity waves (GWs) emanating from the MCS and propagating southward along the stable layer above the CBL provide localized forcing for the actual triggering of initial cells at specific locations. The associated perturbation potential temperature and vertical velocity patterns confirm that the GWs trigger a series of cloud bands. The additional lifting by the updraft of a horizontal convective roll in the CBL underneath the GW updraft works together to promote faster growth of the initial cell that later becomes the supercell. Examination of the Scorer parameter profiles shows favorable conditions for vertical trapping of GWs along the wave guide in the stable layer, preventing the radiation of wave energy to the upper levels.

Climatological Tracking and Lifecycle Characteristics of Mesoscale Convective Systems in Northwestern South America

AbstractMesoscale convective systems (MCSs) are crucial in shaping large‐scale tropical circulation and the hydrological cycle, particularly in Northwestern South America (NwSA), a region marked by complex terrain and significant MCS activity. Understanding MCSs in NwSA is vital due to their impact on precipitation patterns and potential for severe weather events. To enhance this understanding, the ATRACKCS algorithm was developed for tracking convective systems, utilizing precipitation and brightness temperature data sets. This research focuses on documenting the spatiotemporal variability of MCS occurrence, life cycle, and movement. Notably, MCS hotspots were identified to the west of the major orographic features in the region, with maximum occurrences at night, contrasting with the region's typical afternoon peak in land convection. MCS movement is also heavily influenced by topography, with higher velocities on the eastern (windward) side of the Andes compared to velocities on the western (leeward) side. MCSs generally move westward, driven by easterly winds, but this pattern is not consistent throughout the year or region. Northward movement is predominant to the west of the Andes, while southward movement is observed to the east. These seasonal and regional movement variations are linked to factors such as the intertropical convergence zone position, moisture availability, topography, and low‐level jets. This research underscores the complexity of MCSs in NwSA and emphasizes the need for detailed studies on the atmospheric environment shaping these systems. Additionally, it provides a robust 21‐year MCS database for NwSA and an advanced tracking tool for research in various geographic contexts and impact areas.

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

AbstractTo comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

Cell Mergers, Boundary Interactions, and Convective Systems in Cases of Significant Tornadoes and Hail

Abstract Though discrete supercells are usually emphasized in severe weather forecasting, hazard production is often preceded by their interaction with external features. Past studies have examined the impacts of cell mergers, boundaries, other supercells, convective systems, etc. but usually in isolation. Here, we investigate 230 significant tornadoes, 246 significant hail events, and 191 null cases across the United States using WSR-88D data. We find that in over 90% of cases, supercells that produced significant hazards were accompanied by external features. These features varied between hazards; for example, hailstorms were more frequently near boundaries than tornadic storms. That said, the positions of these features with respect to the storm (and storm-relative inflow) distinguished between hazard potential and type. For example, tornadic storms were predominantly on the more unstable side of a boundary, while nontornadic storms and hailstorms were on the less unstable side. Similarly, tornadic storms had more cells in their rear flanks than forward flanks, while hailstorms had more cells in their forward flanks than rear flanks. Although these conditions were observed regardless of the background environment, they were affected by certain variables in the vertical profile, especially in tornadic cases. Namely, when storm-relative inflow was stronger and lifting condensation level (LCL) was lower, tornadic storms were accompanied by more rear-flank cells that were closer to the storm, more directly opposite the storm-relative inflow, for a longer period of time. We propose that these interactions likely modulate hazard potential, in ways that are not accounted for in traditional environmental parameter-based forecasting.

The Evolution of the 2021 Seacor Power Tragedy in Coastal Louisiana

Abstract On 13 April 2021, a mesoscale convective system (MCS) swept across the southeastern Louisiana coast, capsizing the 39-m Seacor Power roughly 7 km from shore and leaving 13 mariners drowned or missing. In addition to the severe straight-line winds that sank the vessel, sustained surface winds > 20 m s−1 behind the leading convection persisted well after the main convective band, inhibiting search and rescue efforts. Though complete historical fatality statistics are unavailable, the 13 deaths associated with this event likely represent one of the deadliest severe convective weather events in modern U.S. maritime history. This analysis integrates in situ, remotely sensed, and reanalysis datasets to reconstruct the 2021 Seacor Power accident as well as ascertain its depiction in day-of operational convection-allowing model (CAM) guidance. Results suggest that the MCS formed along an unanalyzed coastal boundary and developed a strong mesohigh to the east of the wreck as it moved offshore. The resulting zonally oriented pressure gradient directed stiff easterly winds over the wreck for several hours, even as the squall line had propagated well away from the coast. This multihour period of severe weather along the Louisiana coast was relatively well resolved by morning-of CAM guidance, providing optimism that future such events may be anticipated with the lead times required by vulnerable seacraft to reach safe harbor. Future severe convective weather watches containing marine zones might include a “marine” section detailing the potential sea conditions, analogous to the “aviation” section in current severe weather watches.

A Priori Test of Scale-Similarity Parameterizations for Subgrid-Scale Fluxes in Convective Storms

Abstract This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized back-building convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term,” which depends only on the resolved scale components, the “Reynolds term,” which depends only on the SGS components, and the “cross term,” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the countergradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with the filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux. Significance Statement Kilometer-scale numerical weather prediction models can reproduce deep convection explicitly. However, they cannot reproduce smaller inner structures within this deep convection. Therefore, a parameterization scheme that can express the nature of the transport associated with such structures is required. Using a large-eddy simulation with a horizontal resolution sufficient to express such inner structures, we evaluated a parameterization based on the scale similarity assumption, including an empirical coefficient. Based on spectral decomposition, we quantitatively estimated the relationship between the empirical coefficient and grid spacing. Our results provide guidance in selecting the value of the empirical coefficient in this parameterization.

The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) Project

Abstract 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.

Testing a spaceborne passive‐microwave severe hail retrieval over Argentina using ground‐based dual‐polarization radar data

AbstractPassive‐microwave hail detection methods rely on the scattering signal produced by large ice hydrometeors. Recently, passive‐microwave hail retrieval methods have been trained by pairing Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Global Precipitation Measurement (GPM) Microwave Imager (GMI) data with United States surface hail reports to present a global hail climatology. In particular, Southeastern South America (SESA) stands out as a hotspot for hail occurrence, more specifically northeastern Argentina. However, the analysis performed with the available ground hail reports in Argentina shows some differences from the available ground hail reports in Argentina, as it locates the most frequent hail storms in the southwestern (Mendoza and Córdoba) and northeastern (Misiones) regions of SESA. To assess the retrieval from a regional perspective, we take a case study approach and focus on polarization‐corrected temperature features (PCTFs) with a detected . We mainly rely on polarimetric weather radars and a hydrometeor identification algorithm (HID) as a proxy for ground truth. Results indicate that the hail retrieval responds mainly to low 37‐GHz polarized corrected (brightness) temperatures (PCTs) and large PCTF area, while HID outputs show that high probabilities of hail also correspond to abundantly suspended graupel. The observed passive‐microwave hail detection hotspot could be influenced by mesoscale convective systems with deep columns of graupel‐sized frozen hydrometeors, common in northeastern Argentina.