The convective boundary layer(CBL),also known as the mixing layer,constitutes the critical lower segment of the atmosphere that significantly influences daily human activities.The growing demand for precise weather forecasts is driven by the requirements of agriculture,transportation,and routine societal functions.To enhance understanding of the CBL,this study investigates the spatiotemporal variability in the CBL and its controlling factors using four-year Doppler lidar,surface flux,and profiling measurements at five ARM Southern Great Plains sites within a 100 km radius.This investigation utilizes data collected exclusively under clear-sky conditions or scattered low-cloud conditions.Results reveal significant spatial differences in CBL evolutions.Daily mixing layer heights(MLHs)vary up to 1 km(30%of the mean)in late afternoon.There is a clear east-west contrast:western sites(C1,E32,E37)exhibit higher summer MLH(1.9-2.1 km)and vertical velocity variances(1.0-1.2 m2 s-2)than eastern sites(1.6-1.8 km),reversing in winter.Temporally,the MLH peaks at 70%of the sunrise-sunset interval,the lagging heat flux(HF)peaks at 50%;and the seasonal MLH maxima lag the HF by approximately one month,influenced by nighttime PBL(planetary boundary layer)properties.The HF and lower tropospheric stability are the main factors of influence for the CBL,but site-specific dependencies highlight the critical roles of local factors,underscoring the need for including them in CBL modeling.

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.

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Abstract. Mixed-phase clouds play a critical role in Earth’s radiation budget but remain a major source of uncertainty in climate models. Existing satellite climatologies mostly describe mixed-phase clouds in aggregate, without separating cloud types that differ in dynamics, vertical structure, spatial distribution, and microphysical properties. Here we use CloudSat/CALIPSO observations to develop a global, cloud-type-dependent climatology of mixed-phase cloud and examine its spatial, vertical, seasonal, and regional variations. Identified mixed-phase clouds have a global mean occurrence of 18.7 %. Stratus plus stratocumulus (St+Sc) dominates high-latitude mixed-phase occurrence, with local values exceeding 40 % over the Southern Ocean and the Greenland-Iceland-Norwegian seas, whereas altocumulus (Ac) and nimbostratus plus deep convection (Ns+DC) contribute most strongly in midlatitude storm-track regions and convectively active tropical regions. At a given cloud-top temperature, the dominant cloud phase differs substantially among cloud types and regions, indicating that cloud-top temperature alone does not uniquely determine mixed-phase occurrence or phase partitioning. Seasonal and surface contrasts are especially strong for St+Sc: in the NH 45–75° N band, monthly mean occurrence over open ocean increases from about 2–6 % in summer to 24–25 % in winter, whereas in the SH 45–75° S band St+Sc over open ocean reaches its annual minimum in austral summer but over sea ice reaches its minimum in austral winter. These results demonstrate the importance of cloud type for characterizing mixed-phase cloud climatology and provide observational constraints for evaluating the representation of mixed-phase clouds in climate models.

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.

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research utilizing Doppler lidar wind-field data, we optimized the EDR retrieval algorithm using a genetic adaptive approach. The newly developed algorithm demonstrates enhanced accuracy in EDR estimation. The daily evolution of EDR reveals a distinct diurnal pattern in its variation. A detailed four consecutive days study of turbulence generated via low-level jets (LLJs) indicated that EDR driven by heat flux (~10−2 m2/s3) is significantly stronger than that produced through wind shear (~10−3 m2/s3). Subsequently, we examined seasonal variations in EDR at different mixing layer heights (MLH, Zi): elevated EDR values in summer (~7 × 10−3 m2/s3 at 0.1Zi) contrasted with reduced levels in winter (~6 × 10−4 m2/s3 at 0.1Zi). In the early morning, EDR decreases with height for 1 magnitude, while in later stages, it remains relatively stable within 0.1 order of magnitude across 0.1Zi to 0.9Zi. Notably, the EDR during DJF exceeds that of MAM and SON in the afternoon. This suggests that ML turbulence is not solely dependent on surface fluxes (SHF + LHF) but may also be influenced by MLH. A lower MLH (smaller volume), even with reduced surface fluxes, could potentially result in a stronger EDR. Finally, we compared the evolution of the EDR and MLH in the boundary layer using Doppler lidar data from ARM sites and the PBL (Planetary Boundary Layer) Moving Active Profiling System (PBLMAPS) Airborne Doppler Lidar (ADL). The results show that the vertical wind data exhibit strong consistency (R = 0.96) when the ADL is positioned near ARM Southern Great Plains (SGP) sites C1 or E37. The ADL’s mobility and flexibility provide significant advantages for future field experiments, particularly in challenging environments such as mountainous or complex terrains. This study not only highlights the potential of utilizing Doppler lidar alone for EDR calculations but also extensively explores the development patterns of EDR within the ABL.

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.

This study utilizes a 20-yr (2003-22) dataset of tropical deep convective clouds (DCCs) from MODIS wideswath observations with a new detection method to characterize the annual cycle of tropical DCCs, focusing on global patterns, regional variations, and day/night differences. Tropical mean daytime DCC occurrence peaks in December (1.90%) and reaches a minimum in May (1.55%). Notable features include the double ITCZ over the eastern Pacific in March-April and alternating northwest-southeast bands of day/night differences over the Amazon. Regional analyses across six tropical regions, using four DCC size classes (class 1 "small" to class 4 "giant"), reveal distinct behaviors: The western Pacific has the highest DCC occurrences, driven primarily by giant clusters; the eastern Pacific shows similar seasonal trends across all classes; the equatorial Atlantic lacks growth of class 4 clusters; Africa consistently shows larger nighttime cluster sizes; and the Amazon features an alternating day/night dominance in DCC occurrences, with larger nighttime occurrences from May to August but smaller values from October to December. Distributions of normalized DCC cluster number density and occurrence as a function of cluster size show that larger clusters (>10(4) km(2)) dominate total convective area, despite the numerical dominance of smaller clusters (,10(3) km(2)). Oceanic regions exhibit unique patterns of day/night differences, characterized by fewer clusters smaller than 10(2.5) km(2) and larger than 10(5) km(2), but more clusters in the intermediate range during nighttime. These findings provide valuable information for model evaluations, highlighting the importance of resolving DCC cluster size variability in climate models.

The NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W band), and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships [Brown and Francis (BF95); Heymsfield (H14)] and two lidar-radar microphysical retrieval algorithms [CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)] to estimate aircraft-retrieved volume extinction coefficient (a-), ice water content (IWC), and effective radius (re) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R2 = 0.98) with in situ observations of a-. However, only BF95 displays a linear, consistent, and almost temperatureindependent low bias for IWC and re, which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate a-, IWC, and re. For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (from -15 degrees to -10 degrees C) and near freezing (>=-5 degrees C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

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.