Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a K<sub>α</sub>-Band Doppler Radar and a Microwave Radiometer

Frisch, A. S.; Fairall, C. W.; Snider, J. B.

doi:10.1175/1520-0469(1995)052<2788:moscad>2.0.co;2

Cited by 285 publications

(315 citation statements)

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“…Although reflectivity-based methods for retrieving drizzle parameters from groundbased K a -band cloud radars exist (e.g., Frisch et al 1995;Matrosov et al 2004), they are not applicable to rainfall retrievals. As a result, ARM datasets lack vertically resolved quantitative information on rainfall, which is a very important component of the global water cycle that affects climate change.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

Matrosov

May

Shupe

2006

Journal of Atmospheric and Oceanic Technology

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An attenuation-based method to retrieve vertical profiles of rainfall rate from vertically pointing K a -band radar measurements has been refined and adjusted for use with the U.S. Department of Energy's cloud radars deployed at multiple Atmospheric Radiation Program (ARM) test bed sites. This method takes advantage of the linear relationship between the rainfall rate and the attenuation coefficient, and can account for a priori information about the vertical profile of nonattenuated reflectivity. The retrieval method is applied to a wide variety of rainfall events observed at different ARM sites ranging from stratiform events with low-to-moderate rainfall rates (ϳ5 mm h Ϫ1 ) to heavy convective rains with rainfall rates approaching 100 mm h Ϫ1 . The K a -band attenuation-based retrieval results expressed in both instantaneous rainfall rates and in rainfall accumulations are compared to available surface data and measurements of a scanning C-band precipitation polarimetric radar located near the Darwin, Australia, ARM test bed site. The K a -band retrievals are found to be in good agreement with the C-band radar estimates, which are based both on conventional radar reflectivity approaches and on polarimetric differential phase shift measurements. Typically, the C-band-K a -band radar estimate differences are within the expected retrieval uncertainties. The magnitude of the K a -band rainfall-rate estimate error depends on the retrieval resolution, rain intensity, and uncertainties in the profiles of nonattenuated reflectivity. It is shown that reasonable retrieval accuracies (ϳ15%-40%) can be achieved for a large dynamic range of observed rainfall rates (4-100 mm h Ϫ1 ) and the effective vertical resolution of about 1 km. The potential enhancements of the K a -band attenuation-based method by including a priori information on vertical profiles of nonattenuated reflectivity and increasing the height range of the retrievals by using K a -band polarization measurements are also discussed. The addition of the precipitation products to the suite of ARM hydrometeor retrievals can enhance the overall characterization of the vertical atmospheric column.

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Section: Introductionmentioning

confidence: 99%

Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

Matrosov

May

Shupe

2006

Journal of Atmospheric and Oceanic Technology

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“…The Bayesian algorithm has significantly smaller errors for effective radius and optical depth. The similar magnitude errors for the LWC and LWP illustrate that, given the radar reflectivity and a constraint on the total LWP, retrieved LWC is relatively insensitive to the assumptions about the size distribution [Frisch et al, 1998]. However, the effective radius (and therefore the optical depth) is sensitive to assumptions about the shape of the size distribution.…”

Section: Les Test Resultsmentioning

confidence: 81%

“…The method of Frisch et al [1995] assumes the droplet distribution is described by a lognormal distribution with the width of the distribution fixed and number concentration, N, constant with height. Profiles of liquid water content are determined from the radar reflectivity by …”

Section: Les Test Resultsmentioning

confidence: 99%

“…At 35 GHz attenuation due to liquid is approximately 0.65 dB km À1 per g/m 3 of liquid water at 20°C [Lhermitte, 1990]. We make a first order correction for liquid water attenuation by assuming as a first guess that the statistical LWP calculated from the MWR is correct and that LWC / Z 1/2 and calculating the attenuation due to that profile [Frisch et al, 1998]. We then adjust the observed radar reflectivities by the calculated attenuation and use the corrected radar reflectivity profile in the retrieval.…”

Section: Algorithm Detailsmentioning

confidence: 99%

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A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

2002

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[1] We present a new algorithm for retrieving optical depth and liquid water content and effective radius profiles of nonprecipitating liquid water clouds using millimeter wavelength radar reflectivity and dual-channel microwave brightness temperatures. The algorithm is based on Bayes' theorem of conditional probability and combines prior information on cloud microphysics with the remote sensing observations. Prior probability distribution functions for liquid water clouds were derived from the second, third, and sixth moments of droplet size distributions measured by in situ aircraft probes in shallow tropical cumuli. The algorithm also calculates error bars for each retrieved parameter. To assess the algorithm, we perform retrieval simulations using radar reflectivity and brightness temperatures simulated from tropical cumulus fields calculated by a large eddy simulation model with explicit microphysics. These retrieval simulations show that the Bayesian algorithm has similar magnitude errors to current retrieval methods for liquid water content and liquid water path retrievals but has much smaller errors for effective radius and optical depth. We also perform retrievals on three months of data from the Atmospheric Radiation Measurement (ARM) Program's site on Nauru in the tropical west Pacific. For nonprecipitating liquid water clouds over Nauru during June-August 1999 we retrieve a mean optical depth of 9.2, mean liquid water content of 0.112 g/m 3 , and mean effective radius of 7.8 mm. The Bayesian method is a flexible approach to cloud profile remote sensing and could be expanded to other sites or cloud types.

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“…The method is a simplified extension of techniques used previously for radar retrievals of cloud [Frisch et al, 1995;Wilson et al, 1997;Mace et al, 2002;Matrosov et al, 2002;Deng and Mace, 2006]. The precipitation retrieval algorithm description is presented in section 2.…”

Section: Introductionmentioning

confidence: 99%

Ground‐based remote sensing of precipitation in the Arctic

Zhao

Garrett

2008

J. Geophys. Res.

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[1] In this paper, a simple technique is described for application of radar to retrievals of vertical profiles of precipitation in the Arctic. The method is applied specifically to data from the DOE Atmospheric Radiation Measurement (ARM) North Slope of AlaskaAdjacent Arctic Ocean (NSA-AAO) site near Barrow, Alaska. Profiles of precipitation characteristic size are obtained by developing a relationship between particle size and particle fall speed derived from 35 GHz Doppler radar. The relationship depends on an assumed size-and temperature-dependent particle morphology tailored to the Arctic. Error analysis indicates that typical uncertainties in the precipitation retrieval rate are, to 95% confidence, about 40% for ice and 15% for liquid, although they can be as high as a factor of 2. Monthly averaged precipitation retrievals show strong seasonal variation and compare to within respective uncertainties with local ground-based National Weather Service and ARM NSA-AAO surface measurements. Measurements of precipitation profiles show substantial evaporation of precipitation to the atmosphere below cloud base. Associated atmospheric cooling can be several K d À1 , suggesting it may be a substantial component of the boundary layer energy budget in the Arctic.

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Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a K_α-Band Doppler Radar and a Microwave Radiometer

Cited by 285 publications

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Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

Ground‐based remote sensing of precipitation in the Arctic

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Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a Kα-Band Doppler Radar and a Microwave Radiometer

Cited by 285 publications

References 0 publications

Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars

A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

Ground‐based remote sensing of precipitation in the Arctic

Contact Info

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Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a K_α-Band Doppler Radar and a Microwave Radiometer