Search Results

You are looking at 1 - 4 of 4 items for :

  • Author or Editor: Sonia Lasher-Trapp x
  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All Modify Search
Charles A. Doswell III
and
Sonia Lasher-Trapp

Abstract

Meteorological observing networks are nearly always irregularly distributed in space. This irregularity generally has an adverse impact on objective analysis and must be accounted for when designing an analysis scheme. Unfortunately, there has been no completely satisfactory measure of the degree of irregularity, which is of particular significance when designing artificial sampling networks for empirical studies of the impact of this spatial distribution irregularity. The authors propose a measure of the irregularity of sampling point distributions based on the gradient of the sums of the weights used in an objective analysis. Two alternatives that have been proposed, the fractal dimension and a “nonuniformity ratio,” are examined as candidate measures, but the new method presented here is considered superior to these because it can be used to create a spatial “map” that illustrates the spatial structure of the irregularities in a sampling network, as well as to assign a single number to the network as a whole. Testing the new measure with uniform and artificial networks shows that this parameter seems to exhibit the desired properties. When tested with the United States surface and upper-air networks, the parameter provides quantitative information showing that the surface network is much more irregular than the rawinsonde network. It is shown that artificial networks can be created that duplicate the characteristics of the surface and rawinsonde networks; in the case of the surface network, however, a declustered version of the observation site distribution is required.

Full access
Sonia G. Lasher-Trapp
,
William A. Cooper
, and
Alan M. Blyth

Abstract

Ultragiant aerosol particles (UGA) are potentially important for warm rain formation because of their ability to initiate coalescence immediately upon entering a cloud, so it is desirable to obtain local estimates during any field campaign that studies warm rain. Estimates of UGA in clear air from a one-dimensional optical array probe averaged over long time periods from the Small Cumulus Microphysics Study have been published in the literature, but further analysis and comparisons to other probes, presented here, show that the data on which these estimates were based were probably contaminated by noise. A possible explanation for the noise in the probe is given, as are new upper limits, based on few or no particles detected by a two-dimensional optical array probe.

Full access
Dan K. Arthur
,
Sonia Lasher-Trapp
,
Ayman Abdel-Haleem
,
Nicholas Klosterman
, and
David S. Ebert

Abstract

The analysis of diverse datasets from meteorological field campaigns often involves the use of separate 1D or combined 2D plots from various applications, making the determination of spatial and temporal relationships and correlations among these data, and the overall synthesis of information, extremely challenging. Presented here is a new 3D visualization tool, the Aircraft and Radar Data Collocation and Analysis in 3D (ARCA3D), that can combine data collected from different sources and at different scales, utilizing advanced visualization and user interface techniques, which allows for easier comparison and synthesis of such disparate data. The 3D tool is demonstrated with aircraft-based microphysical probe data and ground-based dual-polarization radar data all collected during the Rain in Cumulus over the Ocean (RICO) field campaign. The 3D volumes of radar data can be interactively selected and quantitatively probed, while aircraft-measured variables can be viewed along the aircraft track plotted within the 3D radar volumes or plotted as time series within regions of interest relative to the radar echoes. The greatest benefits of the new software, the 3D viewing of large radar and aircraft datasets with user-driven controls, are difficult to communicate here in a static, 2D written medium, but the application of the tool toward a research problem is presented to elucidate the impacts of these benefits. The ARCA3D software is used to investigate the possible role of giant aerosol particles in the development of precipitation in trade wind cumuli. The temporal trends in the spatial location of the maximum differential reflectivity echoes within the clouds are examined with respect to the ambient giant aerosol number concentration and the measured cloud-base droplet number concentrations on 10 days. The results indicate that in trade wind cumuli of sufficient depth, giant aerosol may determine the original location of the earliest differential reflectivity maximum echo, and thus the first raindrops when present in higher number concentrations. However, when the giant aerosol are less plentiful, the number of cloud droplets activated above the cloud base may also play a role in determining the location of the earliest maximum differential reflectivity echo, and thus the earliest raindrops, in these trade wind cumuli.

Full access
Alexandria Johnson
,
Sonia Lasher-Trapp
,
Aaron Bansemer
,
Z. Ulanowski
, and
Andrew J. Heymsfield

Abstract

The Small Ice Detector, version 2 (SID-2), High-performance Instrumented Airborne Platform for Environmental Research (HIAPER; SID-2H) was used to detect small ice particles in the early stages of ice formation in the high liquid water environment of tropical maritime cumulus clouds sampled during the Ice in Clouds Experiment—Tropical (ICE-T) field campaign. Its performance in comparison to other probes and the development of new corrections applied to the data are presented. The SID-2H detected small ice crystals among larger particles. It correctly identified water drops, and discriminated between round and irregular particle shapes in water-dominated clouds with errors less than 5%. Remaining uncertainties in the sensing volume and the volume over which coincidence of particles occurred, result in the data being used here in a qualitative manner to identify the presence of ice, and its habits and sizes.

Full access