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Abstract Aerosol deep-convective cloud (DCC) interactions remain highly uncertain in the study of water cycles, energy budgets, climate projections, and air quality, partly because it is difficult to disentangle aerosol impacts from the impacts of meteorology in observational studies. Prior studies have shown that increased aerosol ingestion by DCC updrafts can influence their microphysical characteristics through the mixed-phase and condensational aerosol invigoration effects. However, other studies claim that increased aerosol loading produces different microphysical responses that are not consistent with invigoration. This study thus examines the impact of aerosol regimes on DCC microphysics by analyzing ∼1300 DCCs tracked from the Houston–Galveston WSR-88D. Fields from the fifth major global reanalysis produced by ECMWF and Modern-Era Retrospective Analysis for Research and Applications, version 2, are used to estimate meteorological and aerosol conditions near DCCs. DCC tracking was completed using the Multicell Identification and Tracking algorithm applied to radar data. Composite difference contoured frequency by altitude diagrams show statistically significant bulk differences in the vertical structure of dual-polarization radar data that are consistent with previous studies. The probabilistic differences in radar variables were typically 1%–6% above the freezing level and <4% below the freezing level. Microphysical fingerprint distributions showed that DCCs under high aerosol loading exhibit decreased warm rain, increased freezing rates, and increased vapor deposition onto ice. These signatures together are found to be consistent with increased aerosol loading leading to less warm rain, more evaporation under high tropospheric moisture conditions leading to less cold rain, and increased riming/accretion in environments with large instability leading to more cold rain. 

Abstract Most operational radar systems to date produce data with volumetric update times that are insufficient for capturing some of the rapidly evolving processes of hailstorms. Recent advances in rapid scan radar technology and innovative approaches to obtain faster scans have paved the way for a better analysis of hailstorms. This study utilizes the National Severe Storms Laboratory (NSSL)’s research Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radar, KOUN, located in Norman, Oklahoma, to examine rapidly evolving polarimetric features in hailstorms. Quantities such as rate of hail descent and changes in the updraft can be calculated through the analysis of these rapidly evolving polarimetric features, which can increase forecaster confidence in hailstorm evolution and hail sizing. Multi-Radar Multi-Sensor (MRMS) system products were generated using KOUN data, which captured the full life cycle of a hailstorm, including the cyclic evolution of hail production and its relation to tornado production. These data were compared to KTLX, located in Oklahoma City, Oklahoma, to explore what features would not be resolved without a rapid-scanning radar system. Rapid-scanning radar observations provided more details on storm-scale processes related to hail, which can aid in operational forecasting and give insight into future operational phased array radar benefits. 

Abstract This study focuses on the application of phased array radars (PARs) to observe tornadoes and their formation. PAR technology for meteorological applications is maturing and may become a valuable tool for the meteorological community. A fully digital PAR offers a range of benefits including adaptive scanning techniques, higher temporal resolution especially via radar imaging modes, and denser vertical sampling to allow for more complete observations of severe hazard structure and evolution. To best understand the benefits of such a system, synthetic PAR observations are generated from archived mobile rapid-scan observations collected by the Rapid X-band Polarimetric radar (RaXPol) to emulate typical operational radar ranges and PAR-enabled scanning strategy effects. In this study, a synthetic PAR data tool is applied to two tornadic cases (24 May 2011 El Reno, Oklahoma, tornado and the 24 May 2016 Dodge City, Kansas, tornadoes) and one non-tornadic case (17 April 2013). Results indicate that, despite increasing standoff ranges and using vertical imaging, a PAR can still observe a similar mode of tornadogenesis (i.e., non-descending TVS) as traditional mobile systems but with a slight delay in observing intensification at increasing standoff ranges and reduced change in measured intensity. The PAR-enabled vertical imaging mode does not eliminate our ability to identify the TVS at different spoiling factors, but changes to the structure of the TVS may have operational implications. We hope that the improved understanding of meteorological benefits from these synthetic PAR data can provide useful insight for fully digital PAR radar placement and warning operations. 

Abstract: Eu5.08Al3Sb6 can be described as a pseudo rock salt-like “(Eu/Al4)Sb” structure. The polycrystalline synthesis of Eu5+xAl3+ySb6 was optimized. The compositions exhibit n-type transport behavior, ultra-low thermal conductivity, and a high Seebeck coefficient. The Eu5.08Al4Sb6 shows a promising zT of 0.8 at a temperature of 873 K. TechnicalInfo: # Optimization of the composition Eu5+xAl3+ySb6 and thermoelectric figure of merit [https://doi.org/10.5061/dryad.1zcrjdg3q](https://doi.org/10.5061/dryad.1zcrjdg3q) ## Description of the data and file structure The data sets are: (1) powder x-ray diffraction (PXRD): PXRD_ChemComm.zip, thermal gravimetric (TG)- Differential scanning calorimetry (DSC): TGDSC_ChemComm.zip and transport data: TransportData_ChemComm.zip **Powder X-ray Diffraction (PXRD):** PXRD_ChemComm.zip PXRD data were collected with a Bruker D8 Advance Eco Diffractometer using Cu Kα radiation from 2θ = 20° —80°  with a step size of 0.02 ˚ and scan rate of 1 s/step at room temperature in air. The polycrystalline samples were ground inside an Ar-filled glove box and plated on a zero-background holder with isopropanol. **Thermogravimetric (TG)/Differential Scanning Calorimetry (DSC):** TGDSC_ChemComm.zip Melting point studies of aluminum excess were done on polished pellet pieces of Eu5+*x*Al3+*y*Sb6 using TG/DSC (STA 449 F3 Jupiter, Netzsch) from room temperature up to 800°C with a heating rate of 10K/min under a 50 mL/min flow of Ar.  A piece of Aluminum metal (Aluminum shot, approx. 4-8mm (0.2-0.3in), Puratronic™, 99.999% (metals basis)) was used as a reference. **Thermal Conductivity:** TransportData_ChemComm.zip Thermal Diffusivity (*D) was measured on thin (1.19 to 1.2 mm thick) slices of Eu5+*x*Al3+*y*Sb6 pellets using a Netzsch LFA 457 Microflash under a flow of high purity Ar with a polished piece of Zr ribbon wrapped around the sample holder to act as an oxygen catcher. The thermal conductivity (κ) was determined from the equation: κ = *D* x ρ X Cp. All the pellets’ density (ρ) was measured multiple times using the Archimedes method using toluene as the liquid. All samples were > 95% of their theoretical crystallographic densities, calculated using the Arrhenius method. **Transport Measurements:** TransportData_ChemComm.zip  A Linseis LSR-3 instrument was used to measure resistivity and Seebeck coefficient employing the four-probe method from 350K to 800K under He atmosphere. The sample geometries were bar-shaped ( 10.5mm x 4mm x 2mm) using 8 mm probes and polished before measurements. Multiple samples were measured to ensure reproducibility, and the data was cross-checked using measurements at JPL and Northwestern. The Seebeck coefficient was measured using a light pulse technique as described above using W/Nb thermocouples. Electrical resistivity (ρ) was measured using the van der Pauw (VDP) technique with a current of 10 mA. Electrical resistivity and Hall effect data were measured using a home-built Hall effect instrument for the samples measured at Northwestern.1 This set-up utilizes a four-point Van der Pauw resistivity measurement with molybdenum leads and a current of 100 mA. Seebeck coefficient data was collected using a home-built two-probe Seebeck instrument with chromel/Nb thermocouples. ### Files and variables #### File: PXRD_ChemComm.zip **Description:** Powder X-ray diffraction data files, collected according to the description provided above. **Variables:** x, y where x = two-theta (˚) and y = X-ray diffraction Intensity (arbitrary units) **Files:** Eu5_08Al3_3Sb6.xy = sample composition Eu5.08Al3.3Sb6 Eu5_08Al4Sb6.xy = sample composition Eu4.08Al4Sb6 Eu5Al3_3Sb6.xy = sample composiiton Eu5Al3.3Sb6 Eu5Al3Sb6.xy = sample composition, Eu5Al3Sb6 Eu5Al4Sb6.xy = sample composition Eu5Al4Sb6 #### File: TGDSC_ChemComm.zip **Description:** Thermogravimetric and differential scanning calorimetry data collected according to the description provided above. **Variables:** Temperature/˚C; Time/min; DSC/(mW/mg); Mass/%; Gas Flow (purge2)/(ml/min); Gas Flow(protective)/(ml/min); Sensit./(uV/mW) **Files:** ExpDat_Aluminum_shot_800C_1.csv = (segment 1) experimental data for aluminum shot taken from 20 ˚C to 800 ˚C. ExpDat_Aluminum_shot_800C_2.csv = (segment 2) experimental data for aluminum shot taken from 800 ˚C to 20˚C. ExpDat_Eu5_08Al3_3Sb6_800C_1.csv = (segment 1) experimental data for sample composition Eu5.08Al3.3Sb6 taken from 20 ˚C to 800 ˚C. ExpDat_Eu5_08Al3_3Sb6_800C_2.csv = (segment 2) experimental data for sample composition Eu5.08Al3.3Sb6 taken from 800 ˚C to 20˚C. ExpDat_Eu5_08Al4Sb6_800C_1.csv = (segment 1) experimental data for sample composition Eu5.08Al4Sb6 taken from 20 ˚C to 800 ˚C. ExpDat_Eu5_08Al4Sb6_800C-2.csv = (segment 2) experimental data for sample composition Eu5.08Al4Sb6 taken from 800 ˚C to 20˚C. ExpDat_Eu5Al3_3Sb6_800C_1.csv = (segment 1) experimental data for sample composition Eu5Al3.3Sb6 taken from 20 ˚C to 800 ˚C. ExpDat_Eu5Al3_3Sb6_800C_2.csv = (segment 2) experimental data for sample composition Eu5Al3.3Sb6 taken from 800 ˚C to 20 ˚C. ExpDat_Eu5Al4Sb6_800C_1.csv = (segment 1) experimental data for sample composition Eu5Al4Sb6 taken from 20 ˚C to 800 ˚C. ExpDat_Eu5Al4Sb6_800C_2.csv = (segment 2) experimental data for sample composition Eu5Al4Sb6 taken from 800 ˚C to 20 ˚C. #### File: TransportData_ChemComm.zip **Description:** Hall data and Seebeck data files containing Temperature dependent Hall resistivity, mobility, resistivity, Seebeck. *Variables for Hall Data:* Sample thickness in mm; Temperature/(˚C) ; Temperature/(K); Resistivity/(mohm.cm); Conductivity/(S/cm); Carrier Concentration/(/cm3); Hall Mobility/ (cm2/Vs); Hall Resistivity = R_H/(cm3/C); temperature for resistance = T_Res/(˚C); temperature for Hall data collection = T_Hall/(˚C); Resistance Standard deviation = Res*_*Std/(%); Hall resistance standard deviation = RH*_*Std/(%); Hall mobility standard deviation = muH Std/(%); Applied magnetic field = B field/(Gauss); applied current for resistivity = I_Res/(mA); van der Pauw ration = vdP ratio; resistivity time = Res. Time= time and date for the measurement; Hall time = Hall. Time = time and date for the Hall measurement; lead resistance for the 4 leads = Lead Res. 1, Lead Res. 2, Lead Res. 3, and Lead Res. 4 in Ohm; Probe to ground resistance = Probe-GND REsistance/(Ohm); thermocouple 1 = TC1/(˚C) was not used; thermocouple 2 = TC2/(˚C) was not used. **Variables for Seebeck Data**: Temperature = T/(˚C); Seebeck coefficient = seebfit/(uV/K) **Files:** Eu5_08Al3_3Sb6_HallData.csv = Hall data for sample composition Eu5.08Al3.3Sb6 taken from 20 ˚C to 550 ˚C. Eu5_08Al4Sb6_HallData.csv = Hall data for sample composition Eu5.08Al4Sb6 taken from 20˚C to 550 ˚C. Eu5Al3_3Sb6_HallData.csv = Hall data for sample composition Eu5Al3.3Sb6 taken from 20˚C to 550 ˚C. Eu5Al4Sb6_HallData.csv= Hall data for sample composition Eu5Al4Sb6 taken from 20˚C to 550˚C. Eu5_08Al3_3Sb6_Seebeck.csv = Seebeck data for sample composition Eu5.08Al3.3Sb6 taken from 50 ˚C to 600 ˚C. Eu5_08Al4Sb6_Seebeck.csv = Seebeck data for sample composition Eu5.08Al5Sb6 taken from 50 ˚C to 600 ˚C. Eu5Al3_3Sb6_Seebeck.csv = Seebeck data for sample composition Eu5Al3.3Sb6 taken from 50˚C to 600 ˚C. Eu5Al4Sb6_Seebeck.csv = Seebeck data for sample composition Eu5Al4Sb6 taken from 50˚C to 600 ˚C. **ThermalConductivity Folder:** thermal diffusivity data of all samples; conversion to thermal conductivity data provided in LJGEu5Al3Sb6*_*TD_TC.xlsx file. **Variables:** Sample thickness = #Thickness_RT/(mm); sample diameter = #Diameter/(mm); argon gas flow = #Gas flow/(ml/min); shot number; data collection time = #Time/(min); temperature = #Temperature/(˚C); diffusivity data = #Diffusivity/(mm^2/s); standard deviation based on 3 shots = #Std_Dev/(mm^2/s) Files:  Eu5_08Al3_3Sb6TD.csv = thermal diffusivity data for sample composition Eu5.08Al3.3Sb6 taken from 10 ˚C to 550 ˚C. Eu5_08Al4Sb6_TD.csv = thermal diffusivity data for sample composition Eu5.08Al4Sb6 taken from 10 ˚C to 550 ˚C. Eu5Al3_3Sb6_TD.csv = thermal diffusivity data for sample composition Eu5.08Al3.3Sb6 taken from 10 ˚C to 550 ˚C. Eu5Al4Sb6_TD.csv = thermal diffusivity data for sample composition Eu5Al4Sb6 taken from 10 ˚C to 550 ˚C. LJGEu5Al3Sb6_TD_TC.xlsx = Data file providing the conversion of all compositions from thermal diffusivity data to thermal conductivity values. All compositions indicated with headers with temperature = T/(˚C); temperature = T/(K); diffusivity (mm2/s); density /(gm/cm3); Heat Capacity/ ; Thermal Conductivity/(W/mK); Thermal Conductivity/(mW/cmK); 

Abstract Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations. 

Abstract Convective clouds play an important role in the 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. Towards improving 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 - 30 Sept. 2022 in the vicinity of Houston, TX 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 on 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 Sept. 2022, a dual-polarization C-band radar was deployed and operated using a novel, multi-sensor agile adaptive sampling strategy to track the entire lifecycle 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. 

Abstract The time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (Z DR ) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed. 

Abstract Tornadic supercells moved across parts of Oklahoma on the afternoon and evening of 9 May 2016. One such supercell, while producing a long-lived tornado, was observed by nearby WSR-88D radars to contain a strong anticyclonic velocity couplet on the lowest elevation angle. This couplet was located in a very atypical position relative to the ongoing cyclonic tornado and to the supercell’s updraft. A storm survey team identified damage near where this couplet occurred, and, in the absence of evidence refuting otherwise, the damage was thought to have been produced by an anticyclonic tornado. However, such a tornado was not seen in near-ground, high-resolution radar data from a much closer, rapid-scan, mobile radar. Rather, an elongated velocity couplet was observed only at higher elevation angles at altitudes similar to those at which the WSR-88D radars observed the strong couplet. This paper examines observations from two WSR-88D radars and a mobile radar from which it is argued that the anticyclonic couplet (and a similar one ~10 min later) were actually quasi-horizontal vortices centered ~1–1.5 km AGL. The benefits of having data from a radar much closer to the convective storm being sampled (e.g., better spatial resolution and near-ground data coverage) and providing more rapid volume updates are readily apparent. An analysis of these additional radar data provides strong, but not irrefutable, evidence that the anticyclonic tornado that may be inferred from WSR-88D data did not exist; consequently, upon discussions with the National Weather Service, it was not included in Storm Data. 

Abstract This study presents an investigation into relationships among topographic elevation, surface land cover, and tornado intensity using rapid scan, mobile Doppler radar observations of four tornadoes from the U.S. Central Plains. High spatiotemporal resolution observations of tornadic vortex signatures from the radar’s lowest elevation angle data (in most cases ranging from ~100 to 350 m above ground level) are coupled with digital elevation model (DEM) and 2011 National Land Cover Database (NLCD) data using a geographic information system (GIS). The relationships between 1) tornado intensity and topographic elevation or surface roughness and 2) changes in tornado intensity and changes in topographic elevation or surface roughness are investigated qualitatively, and statistical relationships are quantified and analyzed using a bootstrap permutation method for individual case studies and all cases collectively. Results suggest that there are statistically significant relationships for individual cases, but the relationships defy generalization and are different on a case-by-case basis, which may imply that they are coincidental, indicating a null correlation.