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David M. Babb, Johannes Verlinde, and Bruce A. Albrecht

Abstract

A technique is presented that uses Doppler spectra collected from a vertically pointing 94-GHz radar to reconstruct cloud and precipitation drop size distributions. A conceptual model describing the broadening of Doppler spectra by turbulence was adapted from earlier works presented in the literature. This model was then used as a basis for an algorithm that solves for parameters describing the turbulence and drop distribution. Numerically simulated Doppler spectra, calculated from known drop distributions, were first used to test the accuracy of the retrieval algorithm. The tests indicate that the retrieval algorithm can accurately retrieve the turbulence parameter and characteristic diameter but is less able to correctly determine the shape parameter. The technique was then applied to actual Doppler spectra collected from a liquid-phase stratus cloud. Vertical profiles of cloud properties such as liquid water content (LWC), effective radius, total number concentration, and mean vertical wind were obtained. The LWC profiles compared well with concurrent aircraft observations both in magnitude and profile shape. Integrated liquid water path agreed with microwave radiometer observations. A discussion is also presented on the limitations of the retrieval algorithm and the feasibility of retrieving cloud microphysical properties in a variety of situations.

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Scott E. Giangrande, David M. Babb, and Johannes Verlinde

Abstract

Spectral processing algorithms employed in millimeter-wave profiling radars typically obtain good signal-to-noise ratios from weakly scattering clouds by incoherently averaging many spectra. Radar operating characteristics dictate sampling times on the order of a few seconds. Presented here are analyses showing that changes in the vertical wind during the sampling period can be a major contributor to the measured spectrum width. Such broadened spectra violate the assumptions made in spectral inversion techniques, and may lead to incorrect interpretations of the turbulent and microphysical characteristics of the radar volume. Moreover, it is shown that there are several factors involved in determining the measured spectral shape: the averaging time window and horizontal advection velocity of the cloud, as well as horizontal inhomogeneities in cloud vertical velocity and microphysical fields. Current processing algorithms do not allow for distinction between these effects, leading to potential for large errors in retrievals. In this paper a simple technique is presented to remove this effect for monomodal spectra. A side product of this algorithm is high temporal resolution estimates of the volume-mean vertical wind.

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David M. Babb, Johannes Verlinde, and Bert W. Rust

Abstract

Remote sensing instruments have the ability to collect data over extensive temporal periods and spatial regions. A common thread between all these sensors is the need to relate the measured quantity to a meaningful observation of a system property. If the relationship between each measurement and the set of atmospheric quantities that influence that measurement is known, the problem can be reduced to a set of linear equations. Solving for the unknown atmospheric quantities then becomes a linear algebra problem, where the solution vector is equal to the inverse of the kernel matrix multiplied by the set of independent measurements. However, in most remote sensing applications, inversion of the kernel matrix is unstable, resulting in the amplification of measurement and computational uncertainties. Techniques to circumvent this error amplification have focused on methods of constraining the solution. In this paper, the authors adapt an existing technique to do such an inversion. Noise reduction is accomplished by the addition of double-sided inequality constraints for each unknown variable. The advantage of such a technique is the ability to individually adjust the solution space of each individual unknown, depending on a priori knowledge.

The inversion algorithm is applied to the problem of retrieving radar Doppler spectra, which have been artificially broadened by turbulent air motions. First, to test the algorithm, radar Doppler spectra were simulated using known drop size and vertical air motion distributions. The simulated spectra were used as input to the retrieval algorithm, and the results were compared to the initial quiet-air spectrum. Results indicate that accurate retrievals can be performed despite the addition of moderate amounts of noise to the simulated spectra. Then, to demonstrate the practical retrieval of quiet-air Doppler spectra, the algorithm was used to process radar observations collected from continental stratocumulus. From these retrievals, a two-dimensional map of the large-scale vertical motions within the cloud was constructed as well as a profile of vertical velocity variance. In addition, a drop size distribution was also derived from an updraft region of the cloud.

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Lihua Li, Stephen M. Sekelsky, Steven C. Reising, Calvin T. Swift, Stephen L. Durden, Gregory A. Sadowy, Steven J. Dinardo, Fuk K. Li, Arlie Huffman, Graeme Stephens, David M. Babb, and Hans W. Rosenberger

Abstract

Cloud measurements at millimeter-wave frequencies are affected by attenuation due to atmospheric gases, clouds, and precipitation. Estimation of the true equivalent radar reflectivity, Z e, is complicated because extinction mechanisms are not well characterized at these short wavelengths. This paper discusses cloud radar calibration and intercomparison of airborne and ground-based radar measurements and presents a unique algorithm for attenuation retrieval. This algorithm is based on dual 95-GHz radar measurements of the same cloud and precipitation volumes collected from opposing viewing angles. True radar reflectivity is retrieved by combining upward-looking and downward-looking radar profiles. This method reduces the uncertainty in radar reflectivity and attenuation estimates, since it does not require a priori knowledge of hydrometeors' microphysical properties. Results from this technique are compared with results retrieved from the Hitschfeld and Bordan algorithm, which uses single-radar measurements with path-integrated attenuation as a constraint. Further analysis is planned to employ this dual-radar algorithm in order to refine single-radar attenuation retrieval techniques, which will be used by operational sensors such as the CloudSat radar.

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