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Regional Downscaling for Air Quality Assessment
A Reasonable Proposition?
Assessing future changes in air quality using downscaled climate scenarios is a relatively new application of the dynamical downscaling technique. This article compares and evaluates two downscaled simulations for the United States made using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model with the goal of understanding how errors in the downscaled climate simulations may introduce uncertainty in air quality assessment. The two downscaled simulations were driven by boundary conditions from the NCEP–NCAR global reanalysis and a global climate simulation generated by the Goddard Institute for Space Studies global circulation model, respectively. Comparisons of the model runs are made against the boundary layer and circulation characteristics of the North American Regional Reanalysis, and also against observed precipitation. The relative dependence of different simulated quantities on regional forcing, model parameterizations, and large-scale circulation provides a framework to understand similarities and differences between model simulations. Results show significant improvements in the downscaled diurnal wind patterns, in response to the complex orography, that are important for air quality assessment. Evaluation of downscaled boundary layer depth and winds, precipitation, and large-scale circulation shows larger biases related to model physics and biases in the GCM large-scale conditions. Based on the comparisons, recommendations are made to improve the utility of downscaled scenarios for air quality assessment.
Assessing future changes in air quality using downscaled climate scenarios is a relatively new application of the dynamical downscaling technique. This article compares and evaluates two downscaled simulations for the United States made using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model with the goal of understanding how errors in the downscaled climate simulations may introduce uncertainty in air quality assessment. The two downscaled simulations were driven by boundary conditions from the NCEP–NCAR global reanalysis and a global climate simulation generated by the Goddard Institute for Space Studies global circulation model, respectively. Comparisons of the model runs are made against the boundary layer and circulation characteristics of the North American Regional Reanalysis, and also against observed precipitation. The relative dependence of different simulated quantities on regional forcing, model parameterizations, and large-scale circulation provides a framework to understand similarities and differences between model simulations. Results show significant improvements in the downscaled diurnal wind patterns, in response to the complex orography, that are important for air quality assessment. Evaluation of downscaled boundary layer depth and winds, precipitation, and large-scale circulation shows larger biases related to model physics and biases in the GCM large-scale conditions. Based on the comparisons, recommendations are made to improve the utility of downscaled scenarios for air quality assessment.
Abstract
A new methodology to study the Madden–Julian oscillation (MJO) is introduced. While previous MJO studies typically have involved highly simplified mathematical models or general circulation models, this new approach seeks to reproduce the MJO by using a regional model with prescribed boundary conditions. This paper reports initial control run results for this methodology using the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) for a domain extending from the western Indian Ocean to the date line. The control run boundaries are forced using the NCEP–NCAR reanalysis (NRA) dataset for a 24-month time period. The climatology for the 24-month period is examined to establish the robustness of MM5 for this region. Results indicate good agreement in the mean winds between the model and the forcing dataset. The primary differences are an easterly bias at 850 hPa and altered flow patterns in the Indian monsoon region. Mean outgoing longwave radiation (OLR) results are good for the model interior with larger discrepancies near the western and eastern boundaries. These discrepancies lead to a reversal of the OLR gradient along the equator.
Thirty- to seventy-day bandpassed data are examined to determine how MM5 reproduces the MJO. The modeled and comparison 30–70-day zonal wind and OLR data have similar MJO periodicities, exhibit eastward propagation, and possess the observed seasonal character and vertical structure of the MJO. The “Matthews EOF technique” reveals good similarity between the model and observed OLR. Analysis of vertical profiles of 30–70-day zonal wind reveals that lower-tropospheric winds blow in the opposite direction of upper-level winds for both the model and NRA. Vertical profiles of 30–70-day moist static energy exhibit a peak near the top of the boundary layer. Differences between the model-simulated and observed MJO events have a tendency for the OLR to be relatively noisy and for peak OLR intensity to occur in the west Indian Ocean in the model, as opposed to the eastern Indian Ocean in observations. This paper establishes the groundwork for a successive paper wherein the boundary forcings will be modified to examine how this alters the modeled MJO.
Abstract
A new methodology to study the Madden–Julian oscillation (MJO) is introduced. While previous MJO studies typically have involved highly simplified mathematical models or general circulation models, this new approach seeks to reproduce the MJO by using a regional model with prescribed boundary conditions. This paper reports initial control run results for this methodology using the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) for a domain extending from the western Indian Ocean to the date line. The control run boundaries are forced using the NCEP–NCAR reanalysis (NRA) dataset for a 24-month time period. The climatology for the 24-month period is examined to establish the robustness of MM5 for this region. Results indicate good agreement in the mean winds between the model and the forcing dataset. The primary differences are an easterly bias at 850 hPa and altered flow patterns in the Indian monsoon region. Mean outgoing longwave radiation (OLR) results are good for the model interior with larger discrepancies near the western and eastern boundaries. These discrepancies lead to a reversal of the OLR gradient along the equator.
Thirty- to seventy-day bandpassed data are examined to determine how MM5 reproduces the MJO. The modeled and comparison 30–70-day zonal wind and OLR data have similar MJO periodicities, exhibit eastward propagation, and possess the observed seasonal character and vertical structure of the MJO. The “Matthews EOF technique” reveals good similarity between the model and observed OLR. Analysis of vertical profiles of 30–70-day zonal wind reveals that lower-tropospheric winds blow in the opposite direction of upper-level winds for both the model and NRA. Vertical profiles of 30–70-day moist static energy exhibit a peak near the top of the boundary layer. Differences between the model-simulated and observed MJO events have a tendency for the OLR to be relatively noisy and for peak OLR intensity to occur in the west Indian Ocean in the model, as opposed to the eastern Indian Ocean in observations. This paper establishes the groundwork for a successive paper wherein the boundary forcings will be modified to examine how this alters the modeled MJO.
Abstract
The results of an experiment designed to isolate the initiation phase of the Madden–Julian oscillation (MJO) from 30–70-day boundary effects is presented. The technique used to accomplish this involves employing the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), as first presented in the companion paper to this paper. Two runs, each 2 yr long, are integrated forward from 1 June 1990. The first run, called the control, uses the unmodified National Centers for Environmental Prediction (NCEP)–NCAR reanalysis (NRA) dataset for boundary conditions. The second run, called the notched, uses the same NRA dataset for the boundary conditions, with the exception that all signals with periodicities in the 30–70-day range have been removed. Any signals in the 30–70-day range subsequently generated by the notched run are then solely due to signals generated from within the model domain or from signals entering through the domain boundaries with frequencies outside of the MJO band. Comparisons between 2-yr means from each run indicate that filtering the boundaries does not significantly modify the model climatology. The mean wind structure, thermodynamic state, and outgoing longwave radiation (OLR) are almost identical in the control and notched runs. A 30–70-day bandpass filter is used to isolate MJO-like signals in the runs. Comparisons of 30–70-day bandpassed zonal wind, moist static energy (MSE), and OLR reveal that the notched run develops many of the expected characteristics of MJO episodes, but with a weaker signal. Large-scale, organized structures develop that possess seasonal shifts in amplitude, mirroring observed MJO activity, have opposite wind directions in the upper and lower troposphere, and propagate eastward during most strong episodes. The results suggest that neither remnants from previous MJO episodes nor extratropical feedbacks within the MJO time band are necessary for MJO initiation. However, the control run is more organized than the notched run, implying that 30–70 signals outside the model domain influence the MJO signal. There is also some evidence that the recharge–discharge mechanism plays a role in MJO formation.
Abstract
The results of an experiment designed to isolate the initiation phase of the Madden–Julian oscillation (MJO) from 30–70-day boundary effects is presented. The technique used to accomplish this involves employing the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), as first presented in the companion paper to this paper. Two runs, each 2 yr long, are integrated forward from 1 June 1990. The first run, called the control, uses the unmodified National Centers for Environmental Prediction (NCEP)–NCAR reanalysis (NRA) dataset for boundary conditions. The second run, called the notched, uses the same NRA dataset for the boundary conditions, with the exception that all signals with periodicities in the 30–70-day range have been removed. Any signals in the 30–70-day range subsequently generated by the notched run are then solely due to signals generated from within the model domain or from signals entering through the domain boundaries with frequencies outside of the MJO band. Comparisons between 2-yr means from each run indicate that filtering the boundaries does not significantly modify the model climatology. The mean wind structure, thermodynamic state, and outgoing longwave radiation (OLR) are almost identical in the control and notched runs. A 30–70-day bandpass filter is used to isolate MJO-like signals in the runs. Comparisons of 30–70-day bandpassed zonal wind, moist static energy (MSE), and OLR reveal that the notched run develops many of the expected characteristics of MJO episodes, but with a weaker signal. Large-scale, organized structures develop that possess seasonal shifts in amplitude, mirroring observed MJO activity, have opposite wind directions in the upper and lower troposphere, and propagate eastward during most strong episodes. The results suggest that neither remnants from previous MJO episodes nor extratropical feedbacks within the MJO time band are necessary for MJO initiation. However, the control run is more organized than the notched run, implying that 30–70 signals outside the model domain influence the MJO signal. There is also some evidence that the recharge–discharge mechanism plays a role in MJO formation.
Abstract
A new treatment for shallow clouds has been introduced into the Weather Research and Forecasting Model (WRF). The new scheme, called the cumulus potential (CuP) scheme, replaces the ad hoc trigger function used in the Kain–Fritsch cumulus parameterization with a trigger function related to the distribution of temperature and humidity in the convective boundary layer via probability density functions (PDFs). An additional modification to the default version of WRF is the computation of a cumulus cloud fraction based on the time scales relevant for shallow cumuli. Results from three case studies over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) site in north-central Oklahoma are presented. These cases were selected because of the presence of shallow cumuli over the ARM site. The modified version of WRF does a much better job predicting the cloud fraction and the downwelling shortwave irradiance than control simulations utilizing the default Kain–Fritsch scheme. The modified scheme includes a number of additional free parameters, including the number and size of bins used to define the PDF, the minimum frequency of a bin within the PDF before that bin is considered for shallow clouds to form, and the critical cumulative frequency of bins required to trigger deep convection. A series of tests were undertaken to evaluate the sensitivity of the simulations to these parameters. Overall, the scheme was found to be relatively insensitive to each of the parameters.
Abstract
A new treatment for shallow clouds has been introduced into the Weather Research and Forecasting Model (WRF). The new scheme, called the cumulus potential (CuP) scheme, replaces the ad hoc trigger function used in the Kain–Fritsch cumulus parameterization with a trigger function related to the distribution of temperature and humidity in the convective boundary layer via probability density functions (PDFs). An additional modification to the default version of WRF is the computation of a cumulus cloud fraction based on the time scales relevant for shallow cumuli. Results from three case studies over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) site in north-central Oklahoma are presented. These cases were selected because of the presence of shallow cumuli over the ARM site. The modified version of WRF does a much better job predicting the cloud fraction and the downwelling shortwave irradiance than control simulations utilizing the default Kain–Fritsch scheme. The modified scheme includes a number of additional free parameters, including the number and size of bins used to define the PDF, the minimum frequency of a bin within the PDF before that bin is considered for shallow clouds to form, and the critical cumulative frequency of bins required to trigger deep convection. A series of tests were undertaken to evaluate the sensitivity of the simulations to these parameters. Overall, the scheme was found to be relatively insensitive to each of the parameters.
ABSTRACT
The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.
ABSTRACT
The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.
Abstract
The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.
Abstract
The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.
Abstract
Representation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.
Abstract
Representation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.
Abstract
Atmospheric properties in a convective boundary layer vary over a wide range of spatial scales and are commonly studied using large-eddy simulations (LES) in various configurations. We examine how the boundary layer depth and distribution of variability across scales are affected by LES grid spacing, domain size, inhomogeneity of surface properties, and external forcing. Two different setups of the Weather Research and Forecasting (WRF) Model are analyzed. A semi-idealized configuration uses a periodic domain, flat surface, prescribed homogeneous surface heat fluxes, and horizontally uniform profiles of large-scale advective tendencies. A nested LES setup employs a larger domain and realistic initial and boundary conditions, including an interactive land surface model with representative topography and vegetation and soil types. Subdomains of identical size are analyzed for all simulations. Characteristic structure sizes are quantified using the variability scales L 50 and L 95, defined such that features smaller than that contain 50% and 95% of the total variance, respectively. Progressive increase in L 50 from vertical velocity to temperature and moisture structures is systematically reproduced in all simulation configurations. This dependence of L 50 on the considered variable complicates the development of scale-aware parameterizations for models with grid spacing in the “terra incognita.” In simulations using a larger domain with heterogeneous surface properties, the development of internal mesoscale patterns significantly affects variance distributions inside analyzed subdomains. Sizes of boundary layer structures also strongly depend on the LES grid spacing and, in case of heterogeneous surface and topography, on location of the subdomain inside a larger computational domain.
Abstract
Atmospheric properties in a convective boundary layer vary over a wide range of spatial scales and are commonly studied using large-eddy simulations (LES) in various configurations. We examine how the boundary layer depth and distribution of variability across scales are affected by LES grid spacing, domain size, inhomogeneity of surface properties, and external forcing. Two different setups of the Weather Research and Forecasting (WRF) Model are analyzed. A semi-idealized configuration uses a periodic domain, flat surface, prescribed homogeneous surface heat fluxes, and horizontally uniform profiles of large-scale advective tendencies. A nested LES setup employs a larger domain and realistic initial and boundary conditions, including an interactive land surface model with representative topography and vegetation and soil types. Subdomains of identical size are analyzed for all simulations. Characteristic structure sizes are quantified using the variability scales L 50 and L 95, defined such that features smaller than that contain 50% and 95% of the total variance, respectively. Progressive increase in L 50 from vertical velocity to temperature and moisture structures is systematically reproduced in all simulation configurations. This dependence of L 50 on the considered variable complicates the development of scale-aware parameterizations for models with grid spacing in the “terra incognita.” In simulations using a larger domain with heterogeneous surface properties, the development of internal mesoscale patterns significantly affects variance distributions inside analyzed subdomains. Sizes of boundary layer structures also strongly depend on the LES grid spacing and, in case of heterogeneous surface and topography, on location of the subdomain inside a larger computational domain.
Abstract
The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.
Abstract
The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.
