Editorial Type:
Article
Article Type:
Research Article

Particle Size Estimation in Ice-Phase Clouds Using Multifrequency Radar Reflectivity Measurements at 95, 33, and 2.8 GHz

Stephen M. Sekelsky Microwave Remote Sensing Laboratory, University of Massachusetts—Amherst, Amherst, Massachusetts

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Warner L. Ecklund Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado

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John M. Firda Microwave Remote Sensing Laboratory, University of Massachusetts—Amherst, Amherst, Massachusetts

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Kenneth S. Gage NOAA Aeronomy Laboratory, Boulder, Colorado

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Robert E. McIntosh Microwave Remote Sensing Laboratory, University of Massachusetts—Amherst, Amherst, Massachusetts

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Print Publication:
01 Jan 1999
DOI:
https://doi.org/10.1175/1520-0450(1999)038<0005:PSEIIP>2.0.CO;2
Page(s):
5–28
Restricted access

Abstract

Multifrequency radar measurements collected at 2.8 (S band), 33.12 (Ka band), and 94.92 GHz (W band) are processed using a neural network to estimate median particle size and peak number concentration in ice-phase clouds composed of dry crystals or aggregates. The model data used to train the neural network assume a gamma particle size distribution function and a size–density relationship having decreasing density with size. Results for the available frequency combinations show sensitivity to particle size for distributions with median volume diameters greater than approximately 0.2 mm.

Measurements are presented from the Maritime Continent Thunderstorm Experiment, which was held near Darwin, Australia, during November and December 1995. The University of Massachusetts—Amherst 33.12/94.92-GHz Cloud Profiling Radar System, the NOAA 2.8-GHz profiler, and other sensors were clustered near the village of Garden Point, Melville Island, where numerous convective storms were observed. Attenuation losses by the NOAA radar signal are small over the pathlengths considered so the cloud-top reflectivity values at 2.8 GHz are used to remove propagation path losses from the higher-frequency measurements. The 2.8-GHz measurements also permit estimation of larger particle diameters than is possible using only 33.12 and 94.92 GHz. The results suggest that the median particle size tends to decrease with height for stratiform cloud cases. However, this trend is not observed for convective cloud cases where measurements indicate that large particles can exist even near the cloud top.

Corresponding author address: Stephen M. Sekelsky, Dept. of Electrical and Computer Engineering, Knowles 209 C, University of Massachusetts, Amherst, MA 01003.

Abstract

Multifrequency radar measurements collected at 2.8 (S band), 33.12 (Ka band), and 94.92 GHz (W band) are processed using a neural network to estimate median particle size and peak number concentration in ice-phase clouds composed of dry crystals or aggregates. The model data used to train the neural network assume a gamma particle size distribution function and a size–density relationship having decreasing density with size. Results for the available frequency combinations show sensitivity to particle size for distributions with median volume diameters greater than approximately 0.2 mm.

Measurements are presented from the Maritime Continent Thunderstorm Experiment, which was held near Darwin, Australia, during November and December 1995. The University of Massachusetts—Amherst 33.12/94.92-GHz Cloud Profiling Radar System, the NOAA 2.8-GHz profiler, and other sensors were clustered near the village of Garden Point, Melville Island, where numerous convective storms were observed. Attenuation losses by the NOAA radar signal are small over the pathlengths considered so the cloud-top reflectivity values at 2.8 GHz are used to remove propagation path losses from the higher-frequency measurements. The 2.8-GHz measurements also permit estimation of larger particle diameters than is possible using only 33.12 and 94.92 GHz. The results suggest that the median particle size tends to decrease with height for stratiform cloud cases. However, this trend is not observed for convective cloud cases where measurements indicate that large particles can exist even near the cloud top.

Corresponding author address: Stephen M. Sekelsky, Dept. of Electrical and Computer Engineering, Knowles 209 C, University of Massachusetts, Amherst, MA 01003.

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Journal of Applied Meteorology and Climatology

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