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- Author or Editor: William I. Gustafson Jr. x
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Abstract
A long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).
Abstract
A long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).
Abstract
The current paradigm of developing and testing new aerosol process modules is haphazard and slow. Aerosol modules are often tested for short simulation periods using limited data so that their overall performance over a wide range of meteorological conditions is not thoroughly evaluated. Although several model intercomparison studies quantify the differences among aerosol modules, the range of answers provides little insight on how to best improve aerosol predictions. Understanding the true impact of an aerosol process module is also complicated by the fact that other processes—such as emissions, meteorology, and chemistry—are often treated differently. To address this issue, the authors have developed an Aerosol Modeling Testbed (AMT) with the objective of providing a new approach to test and evaluate new aerosol process modules. The AMT consists of a more modular version of the Weather Research and Forecasting model (WRF) and a suite of tools to evaluate the performance of aerosol process modules via comparison with a wide range of field measurements. Their approach systematically targets specific aerosol process modules, whereas all the other processes are treated the same. The suite of evaluation tools will streamline the process of quantifying model performance and eliminate redundant work performed among various scientists working on the same problem. Both the performance and computational expense will be quantified over time. The use of a test bed to foster collaborations among the aerosol scientific community is an important aspect of the AMT; consequently, the longterm development and use of the AMT needs to be guided by users.
Abstract
The current paradigm of developing and testing new aerosol process modules is haphazard and slow. Aerosol modules are often tested for short simulation periods using limited data so that their overall performance over a wide range of meteorological conditions is not thoroughly evaluated. Although several model intercomparison studies quantify the differences among aerosol modules, the range of answers provides little insight on how to best improve aerosol predictions. Understanding the true impact of an aerosol process module is also complicated by the fact that other processes—such as emissions, meteorology, and chemistry—are often treated differently. To address this issue, the authors have developed an Aerosol Modeling Testbed (AMT) with the objective of providing a new approach to test and evaluate new aerosol process modules. The AMT consists of a more modular version of the Weather Research and Forecasting model (WRF) and a suite of tools to evaluate the performance of aerosol process modules via comparison with a wide range of field measurements. Their approach systematically targets specific aerosol process modules, whereas all the other processes are treated the same. The suite of evaluation tools will streamline the process of quantifying model performance and eliminate redundant work performed among various scientists working on the same problem. Both the performance and computational expense will be quantified over time. The use of a test bed to foster collaborations among the aerosol scientific community is an important aspect of the AMT; consequently, the longterm development and use of the AMT needs to be guided by users.
Abstract
Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.
Abstract
Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.