Scientific workflow management systems offer features for composing complex computational pipelines from modular building blocks, for executing the resulting automated workflows, and for recording the provenance of data products resulting from workflow runs. Despite the advantages such features provide, many automated workflows continue to be implemented and executed outside of scientific workflow systems due to the convenience and familiarity of scripting languages (such as Perl, Python, R, and MATLAB), and to the high productivity many scientists experience when using these languages. YesWorkflow is a set of software tools that aim to provide such users of scripting languages with many of the benefits of scientific workflow systems. YesWorkflow requires neither the use of a workflow engine nor the overhead of adapting code to run effectively in such a system. Instead, YesWorkflow enables scientists to annotate existing scripts with special comments that reveal the computational modules and dataflows otherwise implicit in these scripts. YesWorkflow tools extract and analyze these comments, represent the scripts in terms of entities based on the typical scientific workflow model, and provide graphical renderings of this workflow-like view of the scripts. Future versions of YesWorkflow also will allow the prospective provenance of the data products of these scripts to be queried in ways similar to those available to users of scientific workflow systems.
Abstract.During the Central Equatorial Pacific Experiment, ice crystal sizes and shapes were measured in an outflow anvil. A habit (i.e., column, bullet rosette, Koch fractal polycrystal, sphere) was assigned to each particle using a self-organized neural network based on simulations of how the maximum particle dimension and area ratio varied for random orientations of these crystals. Average ice crystal size and shape distributions were calculated for 25 km long segments at six altitudes using measurements from a two-dimensional cloud probe for crystals larger than 90 /zm and a parameterization for smaller crystals based on measurements from the Video Ice Particle Sampler (VIPS). Mean-scattering properties were determined by weighting the size and shape dependent single-scattering properties computed with ray-tracing algorithms according to scattering cross-section. Reflectances at 0.664, 0.875, 1.621, and 2.142 /zm were then calculated using a Monte Carlo radiative transfer routine. Although these reflectances agree reasonably with those measured by the MODIS airborne simulator (MAS) above the anvil, uncertainties in cloud base and system evolution prevent a determination of whether ray-tracing or anomalous diffraction theory better predict reflectance. The calculated reflectances are as sensitive to the numbers and shapes of crystals smaller than 90 /zm as to those of larger crystals. The calculated reflectances were insensitive to the classification scheme (i.e., neural network, discriminator analysis, and previously used classification scheme) for assigning particle shape to observed crystals. However, the reflectances significantly depended on assumed particle shape.
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