Min Deng

Lower-tropospheric stability (LTS) regulates convective boundary-layer growth, low-cloud evolution, pollutant dispersion, and land–atmosphere coupling, yet continuous observations are scarce because thermodynamic profilers are sparse and expensive, and reanalysis products explain less than one-third of the observed variance in inversion strength even at well-instrumented sites. We retrieve continuous LTS at the Atmospheric Radiation Measurement Southern Great Plains observatory from routine surface-flux measurements and Doppler-lidar-derived mixed-layer height, using a regime-aware, stage-partitioned framework established in earlier work. At the central facility over May 2016–December 2020 the retrieval reproduces observed LTS with a coefficient of determination of 0.78 and a mean absolute error of 2.39 K, and captures the full diurnal evolution of stability. The principal contribution is a diagnostic for judging when such a retrieval can be transferred to another site. Predictor-distribution differences, quantified by the Kullback–Leibler divergence, are weighted by SHapley Additive exPlanations sensitivities to form a single metric, ΔKL×|SHAP|. Applied to two extended facilities, the metric shows that distributional differences degrade transferability only where the retrieval is sensitive to the shifted predictor, and identifies cloud-base height, mixed-layer height, and thermodynamic predictors as the dominant contributors at the poorer-performing site. Gaussian perturbation experiments provide an independent fingerprint: monotonic error growth indicates a transferable relationship limited by random observational uncertainty, whereas a non-monotonic response indicates structural thermodynamic mismatch. Requiring only predictor distributions and model sensitivities, the diagnostic applies to any data-driven atmospheric retrieval.

Turbulence dissipation rate (EDR) governs the conversion of turbulent kinetic energy to heat and thereby controls vertical mixing, transport, and energy exchange in the convective boundary layer (CBL). The spatiotemporal structure of EDR remains poorly constrained, as observations are limited to point measurements or sparse vertical profiles that cannot resolve spatiotemporal evolution, hindering evaluation of planetary boundary layer (PBL) parameterizations that rely on simplified turbulence representations. Here we develop a surface‐constrained framework that reconstructs CBL EDR structure from routine surface observations by integrating mixed‐layer physical constraints with a machine‐learning mapping trained on Doppler‐lidar–retrieved EDR at the ARM SGP site. The framework explains ∼70% of the observed EDR variance and reproduces its spatiotemporal structure. Model skill decreases systematically with height ( R ² 0.7 to 0.5 from surface to CBL top), consistent with the transition from surface‐driven to entrainment‐zone dynamics; a height‐resolved feature‐attribution analysis confirms that this decay tracks the diminishing importance of surface‐forcing predictors with height ( r = 0.78). Seasonal analysis shows highest predictability in autumn ( R ² ≈ 0.75) and reduced skill in winter, reflecting regime‐dependent turbulence production. Feature attribution shows that surface heat flux and near‐surface turbulence scales dominate EDR variability, recovering classical similarity scaling through data‐driven inference. Cross‐site evaluation confirms generalization across nearby locations ( R ² ≈ 0.67), indicating that surface forcing is a robust first‐order constraint on CBL turbulence structure. Remaining midday convective biases highlight entrainment and shear processes not captured by surface inputs. These results supply a data‐driven benchmark for evaluating and improving PBL parameterizations.

Accurate beam pointing is essential for vertically pointing cloud radars, as even small tilts away from zenith introduce projections of horizontal wind into Doppler velocity measurements. These effects can bias the interpretation of vertical air motion and hydrometeor fall speeds, leading to systematic errors in cloud and precipitation retrievals. We present the U -normalized velocity–direction display (UN-VDD) method to estimate and validate radar beam pointing angle using collocated radiosonde observations. When the radar beam is tilted, the observed Doppler velocity exhibits a cosine dependence on wind direction due to projection of horizontal wind onto the beam, with an amplitude proportional to wind speed. By normalizing Doppler velocity with horizontal wind speed, this geometric dependence can be isolated, enabling quantitative estimation of off-zenith beam pointing. The method is applied to the Ka-band ARM Zenith Radar (KAZR) and the Marine W-band ARM Cloud Radar (MWACR) during the Cloud and Precipitation Experiment at kennaook (CAPE-k). Results show a clear wind-direction dependence consistent with small but measurable beam pointing offsets. Differences in Doppler velocity between KAZR and MWACR further reduce the influence of hydrometeor fall velocity and vertical air motion, providing an independent constraint on relative pointing errors. Application to KAZR observations at ARM fixed sites and recent field campaigns further demonstrates that the method is robust across a range of atmospheric conditions. This approach provides a practical and scalable tool for evaluating radar beam pointing using routinely available radiosonde data, with direct implications for improving the accuracy of ARM cloud and precipitation products.

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 Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection interactions ExpeRiment (TRACER) campaign. We characterized 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 1000 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 6–7 September 2022 case study demonstrates that mesoscale transport can simultaneously transform the thermodynamic environment and the aerosol population, highlighting the importance of constraining boundary-layer dynamics and airmass origins before attributing cloud changes to aerosol effects in complex coastal environments.

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 macrophysical 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 18.5 %. 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 cloud thermodynamic phase. 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.

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.

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 beam pointing is essential for vertically pointing cloud radars, as even small tilting off zenith introduces projections of horizontal wind into Doppler velocity measurements. These effects can bias the interpretation of vertical air motion and hydrometeor fall speeds, leading to systematic errors in cloud and precipitation retrievals. We present a method to estimate and validate radar beam pointing angle using collocated radiosonde observations. When the radar beam is tilted, the observed Doppler velocity exhibits a cosine dependence on wind direction due to projection of horizontal wind onto the beam, with an amplitude proportional to wind speed. By normalizing Doppler velocity with horizontal wind speed, this geometric dependence can be isolated, enabling quantitative retrieval of off-zenith beam pointing estimation. The method is applied to the Ka-band ARM Zenith Radar (KAZR) and the Marine W-band ARM Cloud Radar (MWACR) during the Cloud and Precipitation Experiment at kennaook (CAPE-k). Results show a clear wind-direction dependence consistent with small but measurable beam pointing offsets. Differences in Doppler velocity between KAZR and MWACR further reduce the influence of hydrometeor fall velocity and vertical air motion, providing an independent constraint on relative pointing errors. Application to KAZR observations at ARM fixed sites and recent field campaigns further demonstrates that the method is robust across a range of atmospheric conditions. This approach provides a practical and scalable tool for evaluating radar beam pointing using routinely available radiosonde data, with direct implications for improving the accuracy of ARM cloud and precipitation products.

Accurate beam pointing is essential for vertically pointing cloud radars, as even small tilting off zenith introduces projections of horizontal wind into Doppler velocity measurements. These effects can bias the interpretation of vertical air motion and hydrometeor fall speeds, leading to systematic errors in cloud and precipitation retrievals. We present a method to estimate and validate radar beam pointing angle using collocated radiosonde observations. When the radar beam is tilted, the observed Doppler velocity exhibits a cosine dependence on wind direction due to projection of horizontal wind onto the beam, with an amplitude proportional to wind speed. By normalizing Doppler velocity with horizontal wind speed, this geometric dependence can be isolated, enabling quantitative retrieval of off-zenith beam pointing estimation. The method is applied to the Ka-band ARM Zenith Radar (KAZR) and the Marine W-band ARM Cloud Radar (MWACR) during the Cloud and Precipitation Experiment at kennaook (CAPE-k). Results show a clear wind-direction dependence consistent with small but measurable beam pointing offsets. Differences in Doppler velocity between KAZR and MWACR further reduce the influence of hydrometeor fall velocity and vertical air motion, providing an independent constraint on relative pointing errors. Application to KAZR observations at ARM fixed sites and recent field campaigns further demonstrates that the method is robust across a range of atmospheric conditions. This approach provides a practical and scalable tool for evaluating radar beam pointing using routinely available radiosonde data, with direct implications for improving the accuracy of ARM cloud and precipitation products.

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.