In this thesis, we introduce a new approach to classify rain clouds based on the relationship between the emission signal and scattering signal derived from microwave brightness temperature data. Two parameters are used as indicators of emission signal and scattering signal respectively: one is the polarization difference (D) at 19 GHz, and the other one is the polarization-corrected temperature (PCT) at high-frequencies channels. D is related to the emission of liquid hydrometeors, and PCT mainly reflects the brightness temperature depression due to the scattering by ice particles. Both D and PCT decrease with increasing precipitation rate. Therefore, certain combinations of D and PCT can be regarded as the representatives of cloud hydrometeor structures. Based on the D-PCT relationship investigated in this study, we classified the observed rain clouds into five categories—non-precipitating, light-precipitating, liquid-dominant precipitating, well-mixed precipitating, and ice-dominant precipitating clouds. We verified the results of the classification of different precipitation cases over tropical regions. For both the hurricane and front cases, the results show that the distributions of categorized cloud pixels can reflect the horizontal structure of the weather systems. The monthly gridded mean frequencies of categorized precipitating clouds are used to analyze the relationship between the seasonal and interannual cycles of tropical precipitation and clouds’ hydrometeor components. Moreover, the results indicated that in an annual cycle or an ENSO cycle, when the local precipitation frequencies increase, the occurrence frequencies of all kinds of rain clouds will increase. However, among those precipitating systems, the proportions of ice-dominant and well-mixed clouds increases while that of water-dominant clouds decrease as the local precipitation increases. Anomalies of the opposite sign tend to accompany the decreasing precipitations situations. Overall, the classification method proves to be useful to extract objective information from observed emission and scattering signals. Since clouds have always been signs of the weather systems, the long-term variances of global distribution and characteristics of rain clouds are as an aspect of cloud climatology. Moreover, the categorization of precipitation types can be useful in developing the best retrieval algorithm of rain rate for a specific cloud type. Additionally, the information about cloud types can be used to improve our understanding of cloud processes and to increase the accuracy of weather and climate models. / A Thesis submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2019. / March 5, 2019. / Classification, Emission, Microwave, Rain clouds, Scattering / Includes bibliographical references. / Guosheng Liu, Professor Directing Thesis; Vasubandhu Misra, Committee Member; Allison Wing, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_709787 |
| Contributors | Li, Jiangmei (author), Liu, Guosheng (Professor Directing Thesis), Misra, Vasubandhu (Committee Member), Wing, Allison A. (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 |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, master thesis |
| Format | 1 online resource (69 pages), computer, application/pdf |
Page generated in 0.0027 seconds