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  • Author or Editor: K. Bower x
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J. M. White
,
J. F. Bowers
,
S. R. Hanna
, and
J. K. Lundquist

Abstract

The mixing depth of the boundary layer is an input to most atmospheric transport and dispersion (ATD) models, which obtain mixing depths in one of four ways: 1) observations by radiosondes, sodars, or other devices; 2) simulations by regional or mesoscale meteorological models; 3) parameterizations based on boundary layer similarity theory; or 4) climatological averages. This paper describes a situation during a field experiment when exceptionally low mixing depths persisted in the morning and led to relatively high observed tracer concentrations. The low mixing depths were caused by synoptic effects associated with a nearby stationary front and the outflow from a mesoscale thunderstorm complex located 20–50 km away. For the same time period, the ATD model-parameterized mixing depth was a factor of 5–10 higher, leading to predicted concentrations that were less than the observations by a factor of 5–10. The synoptic situation is described and local radiosonde and radar observations of mixing depth are presented, including comparisons with other more typical days. Time series of local observations of near-surface sensible heat fluxes are also plotted to demonstrate the suppression of turbulence by negative sensible heat fluxes during the period in question.

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Paul J. Connolly
,
Michael J. Flynn
,
Z. Ulanowski
,
T. W. Choularton
,
M. W. Gallagher
, and
K. N. Bower

Abstract

This paper explains and develops a correction algorithm for measurement of cloud particle size distributions with the Stratton Park Engineering Company, Inc., Cloud Particle Imager (CPI). Cloud particle sizes, when inferred from images taken with the CPI, will be oversized relative to their “true” size. Furthermore, particles will cease to be “accepted” in the image frame if they lie a distance greater than the depth of field from the object plane.

By considering elements of the scalar theory for diffraction of light by an opaque circular disc, a calibration method is devised to overcome these two problems. The method reduces the error in inferring particle size from the CPI data and also enables the determination of the particles distance from the object plane and hence their depth of field. These two quantities are vital to enable quantitative measurements of cloud particle size distributions (histograms of particle size that are scaled to the total number concentration of particles) in the atmosphere with the CPI. By using both glass calibration beads and novel ice crystal analogs, these two problems for liquid drops and ice particles can be quantified.

Analysis of the calibration method shows that 1) it reduces the oversizing of 15-μm beads (from 24.3 to 14.9 μm for the sample mean), 40-μm beads (from 50.0 to 41.4 μm for the sample mean), and 99.4-μm beads (from 103.7 to 99.8 μm for the sample mean); and 2) it accurately predicts the particles distance from the object plane (the relationship between measured and predicted distance shows strong positive correlation and gives an almost one-to-one relationship). Realistic ice crystal analogs were also used to assess the errors in sampling ice clouds and found that size and distance from the object plane could be accurately predicted for ice crystals by use of the particle roundness parameter (defined as the ratio of the projected area of the particle to the area of a circle with the same maximum length). While the results here are not directly applicable to every CPI, the methods are, as data taken from three separate CPIs fit the calibration model well (not shown).

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