Editorial Type:
Article
Article Type:
Research Article

Moisture Analysis of a Type I Cloud-Topped Boundary Layer from Doppler Radar and Rawinsonde Observations

Richard S. Penc Department of Environmental and Atmospheric Sciences, Creighton University, Omaha, Nebraska

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Print Publication:
01 Dec 2001
DOI:
https://doi.org/10.1175/1520-0426(2001)018<1941:MAOATI>2.0.CO;2
Page(s):
1941–1958
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Abstract

Moisture data from radar and rawinsonde observations during three lake-effect snow events are analyzed to determine entrainment rates. Type I convective boundary layers, which are those driven largely by surface heating, typically accompany these storms. Gathered during the winter of 1990, the data are a subset from the Lake Ontario Winter Storms (LOWS) Project, which deployed a mesoscale network of sensors.

Doppler wind profiler signal-to-noise ratio (SNR) data are used to derive humidity structure function parameter (C2q) time–height series analysis, which are then compared to rawinsonde specific humidity (q) plots. Visual comparison of log(C2q) and q analysis indicated a strongly positive correlation. Radar-derived humidity analysis is used to estimate the depth of the Type I (driven by surface heating), cloud-topped boundary layer (CTBL), which corresponded well with results from LOWS rawinsonde data. Calculations of the contribution of (C2q) to the refractive index structure parameter (C2n) showed the humidity correction factor (α2r) to range from 1.02 to 1.04 within the CTBL, consistent with previous findings for Type II CTBLs. A comparison of entrainment rates, computed via two different methods, were in agreement.

Corresponding author address: Dr. Richard Penc, Department of Environmental and Atmospheric Sciences, Creighton University, 2500 California Plaza, Omaha, NE 68178. Email: rspenc@creighton.edu

Abstract

Moisture data from radar and rawinsonde observations during three lake-effect snow events are analyzed to determine entrainment rates. Type I convective boundary layers, which are those driven largely by surface heating, typically accompany these storms. Gathered during the winter of 1990, the data are a subset from the Lake Ontario Winter Storms (LOWS) Project, which deployed a mesoscale network of sensors.

Doppler wind profiler signal-to-noise ratio (SNR) data are used to derive humidity structure function parameter (C2q) time–height series analysis, which are then compared to rawinsonde specific humidity (q) plots. Visual comparison of log(C2q) and q analysis indicated a strongly positive correlation. Radar-derived humidity analysis is used to estimate the depth of the Type I (driven by surface heating), cloud-topped boundary layer (CTBL), which corresponded well with results from LOWS rawinsonde data. Calculations of the contribution of (C2q) to the refractive index structure parameter (C2n) showed the humidity correction factor (α2r) to range from 1.02 to 1.04 within the CTBL, consistent with previous findings for Type II CTBLs. A comparison of entrainment rates, computed via two different methods, were in agreement.

Corresponding author address: Dr. Richard Penc, Department of Environmental and Atmospheric Sciences, Creighton University, 2500 California Plaza, Omaha, NE 68178. Email: rspenc@creighton.edu

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