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Christopher David Westbrook, Robin J. Hogan, and Anthony J. Illingworth

Abstract

A new method of accurately calculating the capacitance of realistic ice particles is described: such values are key to accurate estimates of deposition and evaporation (sublimation) rates in numerical weather models. The trajectories of diffusing water molecules are directly sampled, using random “walkers.” By counting how many of these trajectories intersect the surface of the ice particle (which may be any shape) and how many escape outside a spherical boundary far from the particle, the capacitances of a number of model ice particle habits have been estimated, including hexagonal columns and plates, “scalene” columns and plates, bullets, bullet rosettes, dendrites, and realistic aggregate snowflakes. For ice particles with sharp edges and corners this method is an efficient and straightforward way of solving Laplace’s equation for the capacitance. Provided that a large enough number of random walkers are used to sample the particle geometry (∼104) the authors expect the calculated capacitances to be accurate to within ∼1%. The capacitance for the modeled aggregate snowflakes (C/D max = 0.25, normalized by the maximum dimension D max) is shown to be in close agreement with recent aircraft measurements of snowflake sublimation rates. This result shows that the capacitance of a sphere (C/D max = 0.5), which is commonly used in numerical models, overestimates the evaporation rate of snowflakes by a factor of 2.

The effect of vapor “screening” by crystals growing in the vicinity of one another has also been investigated. The results clearly show that neighboring crystals growing on a filament in cloud chamber experiments can strongly constrict the vapor supply to one another, and the resulting growth rate measurements may severely underestimate the rate for a single crystal in isolation (by a factor of 3 in this model setup).

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David McLaughlin, David Pepyne, V. Chandrasekar, Brenda Philips, James Kurose, Michael Zink, Kelvin Droegemeier, Sandra Cruz-Pol, Francesc Junyent, Jerald Brotzge, David Westbrook, Nitin Bharadwaj, Yanting Wang, Eric Lyons, Kurt Hondl, Yuxiang Liu, Eric Knapp, Ming Xue, Anthony Hopf, Kevin Kloesel, Alfred DeFonzo, Pavlos Kollias, Keith Brewster, Robert Contreras, Brenda Dolan, Theodore Djaferis, Edin Insanic, Stephen Frasier, and Frederick Carr

Dense networks of short-range radars capable of mapping storms and detecting atmospheric hazards are described. Composed of small X-band (9.4 GHz) radars spaced tens of kilometers apart, these networks defeat the Earth curvature blockage that limits today s long-range weather radars and enables observing capabilities fundamentally beyond the operational state-of-the-art radars. These capabilities include multiple Doppler observations for mapping horizontal wind vectors, subkilometer spatial resolution, and rapid-update (tens of seconds) observations extending from the boundary layer up to the tops of storms. The small physical size and low-power design of these radars permits the consideration of commercial electronic manufacturing approaches and radar installation on rooftops, communications towers, and other infrastructure elements, leading to cost-effective network deployments. The networks can be architected in such a way that the sampling strategy dynamically responds to changing weather to simultaneously accommodate the data needs of multiple types of end users. Such networks have the potential to supplement, or replace, the physically large long-range civil infrastructure radars in use today.

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