High frequency microwave instruments are increasingly used to observe ice clouds and snow. These instruments are significantly more sensitive than conventional precipitation radar. This is ideal for analyzing ice-bearing clouds, for ice particles are tenuously distributed and have effective densities that are far less than liquid water. However, at shorter wavelengths, the electromagnetic response of ice particles is no longer solely dependent on particle mass. The shape of the ice particles also plays a significant role. Thus, in order to understand the observations of high frequency microwave radars and radiometers, it is essential to model the scattering properties of snowflakes correctly. Several research groups have proposed detailed models of snow aggregation. These particle models are coupled with computer codes that determine the particles' electromagnetic properties. However, there is a discrepancy between the particle model outputs and the requirements of the electromagnetic models. Snowflakes have countless variations in structure, but we also know that physically similar snowflakes scatter light in much the same manner. Structurally exact electromagnetic models, such as the discrete dipole approximation (DDA), require a high degree of structural resolution. Such methods are slow, spending considerable time processing redundant (i.e. useless) information. Conversely, when using techniques that incorporate too little structural information, the resultant radiative properties are not physically realistic. Then, we ask the question, what features are most important in determining scattering? This dissertation develops a general technique that can quickly parameterize the important structural aspects that determine the scattering of many diverse snowflake morphologies. A Voronoi bounding neighbor algorithm is first employed to decompose aggregates into well-defined interior and surface regions. The sensitivity of scattering to interior randomization is then examined. The loss of interior structure is found to have a negligible impact on scattering cross sections, and backscatter is lowered by approximately five percent. This establishes that detailed knowledge of interior structure is not necessary when modeling scattering behavior, and it also provides support for using an effective medium approximation to describe the interiors of snow aggregates. The Voronoi diagram-based technique enables the almost trivial determination of the effective density of this medium. A bounding neighbor algorithm is then used to establish a greatly improved approximation of scattering by equivalent spheroids. This algorithm is then used to posit a Voronoi diagram-based definition of effective density approach, which is used in concert with the T-matrix method to determine single-scattering cross sections. The resulting backscatters are found to reasonably match those of the DDA over frequencies from 10.65 to 183.31 GHz and particle sizes from a few hundred micrometers to nine millimeters in length. Integrated error in backscatter versus DDA is found to be within 25% at 94 GHz. Errors in scattering cross-sections and asymmetry parameters are likewise small. The observed cross-sectional errors are much smaller than the differences observed among different particle models. This represents a significant improvement over established techniques, and it demonstrates that the radiative properties of dense aggregate snowflakes may be adequately represented by equal-mass homogeneous spheroids. The present results can be used to supplement retrieval algorithms used by CloudSat, EarthCARE, Galileo, GPM and SWACR radars. The ability to predict the full range of scattering properties is potentially also useful for other particle regimes where a compact particle approximation is applicable. / A Dissertation submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2017. / March 21, 2017. / discrete dipole approximation, hydrometeors, microwave instruments, snow, T-matrix method, Voronoi diagrams / Includes bibliographical references. / Guosheng Liu, Professor Directing Dissertation; Max Gunzburger, University Representative; Jon Ahlquist, Committee Member; Robert Ellingson, Committee Member; Zhaohua Wu, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_507678 |
| Contributors | Honeyager, Ryan Erick (authoraut), Liu, Guosheng (professor directing dissertation), Gunzburger, Max D. (university representative), Ahlquist, Jon E. (committee member), Ellingson, R. G. (committee member), Wu, Zhaohua (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Earth, Ocean and Atmospheric Science (degree granting departmentdgg) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (128 pages), computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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