Dataset Information

Simultaneous observations of aerosol-cloud-albedo interactions with three stacked unmanned aerial vehicles.

ABSTRACT: Aerosol impacts on climate change are still poorly understood, in part, because the few observations and methods for detecting their effects are not well established. For the first time, the enhancement in cloud albedo is directly measured on a cloud-by-cloud basis and linked to increasing aerosol concentrations by using multiple autonomous unmanned aerial vehicles to simultaneously observe the cloud microphysics, vertical aerosol distribution, and associated solar radiative fluxes. In the presence of long-range transport of dust and anthropogenic pollution, the trade cumuli have higher droplet concentrations and are on average brighter. Our observations suggest a higher sensitivity of radiative forcing by trade cumuli to increases in cloud droplet concentrations than previously reported owing to a constrained droplet radius such that increases in droplet concentrations also increase cloud liquid water content. This aerosol-cloud forcing efficiency is as much as -60 W m(-2) per 100% percent cloud fraction for a doubling of droplet concentrations and associated increase of liquid water content. Finally, we develop a strategy for detecting aerosol-cloud interactions based on a nondimensional scaling analysis that relates the contribution of single clouds to albedo measurements and illustrates the significance of characterizing cloud morphology in resolving radiometric measurements. This study demonstrates that aerosol-cloud-albedo interactions can be directly observed by simultaneous observations below, in, and above the clouds.

SUBMITTER: Roberts GC

PROVIDER: S-EPMC2396670 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

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Simultaneous observations of aerosol-cloud-albedo interactions with three stacked unmanned aerial vehicles.

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