Abstract. Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles > 20 nm) across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies based on κ–Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of κ. The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating “migrating-CCNCs” to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.
[1] An analytical expression that relates the relative dispersion (ratio of standard deviation to mean radius) of the cloud droplet size distribution to CCN spectra and updraft velocity is derived from adiabatic growth theory of cloud droplets. Coupled with the Twomey expression for droplet concentration, the analytical expression is used to examine the relationship of relative dispersion to droplet concentration under different combinations of CCN spectra and updraft velocities. These analytical results compare favorably with the corresponding simulations of an adiabatic parcel model. The analytical expression theoretically demonstrates that an increase in aerosol loading (CCN concentration) leads to concurrent increases in the droplet concentration and relative dispersion whereas a larger updraft velocity leads to a higher droplet concentration but a smaller relative dispersion. Citation: Liu, Y., P. H. Daum, and S. S. Yum (2006), Analytical expression for the relative dispersion of the cloud droplet size distribution, Geophys.
Continuous aircraft measurements of cloud condensation nuclei (CCN) were made during 16 summertime flights in eastern Florida. The air masses were divisible into maritime and continental regimes that respectively corresponded to wind direction-easterly (onshore) and westerly (offshore). Throughout these small cumulus clouds there were consistently higher concentrations of smaller droplets in the continental air. There was much more drizzle (diameter Ͼ 50 m) in the maritime clouds where drizzle was associated with larger mean cloud droplet (2-50-m diameter) sizes, higher concentrations of large cloud droplets, and greater amounts of cloud droplet liquid water. An apparent cloud droplet mean size threshold for the onset of drizzle was almost never exceeded in the continental clouds but was often exceeded in the maritime clouds, especially at higher altitudes. All together these results demonstrate that higher CCN concentrations suppressed drizzle.
This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility (Vis) and ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are also functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in-situ observations at the surface and in the cloud, as well as aircraft and Unmanned Aerial Vehicles (UAV) mounted sensors, are becoming more common. Because of prediction issues at smaller time and space scales (e.g., <1 km), meteorological forecasts from NWP models need to be continuously improved. Aviation weather forecasts also need to be developed to provide information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.
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