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Classification of Rain Clouds Based on the Relationship between Microwave Emission and Scattering Signals

Title: Classification of Rain Clouds Based on the Relationship between Microwave Emission and Scattering Signals.
Name(s): Li, Jiangmei, author
Liu, Guosheng, (Professor of meteorology), Professor Directing Thesis
Misra, Vasubandhu, 1970-, 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 department
Type of Resource: text
Genre: Text
Master Thesis
Issuance: monographic
Date Issued: 2019
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (69 pages)
Language(s): English
Abstract/Description: 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.
Identifier: 2019_Spring_Li_fsu_0071N_15013 (IID)
Submitted Note: 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.
Degree Awarded: Spring Semester 2019.
Date of Defense: March 5, 2019.
Keywords: Classification, Emission, Microwave, Rain clouds, Scattering
Bibliography Note: Includes bibliographical references.
Advisory Committee: Guosheng Liu, Professor Directing Thesis; Vasubandhu Misra, Committee Member; Allison Wing, Committee Member.
Subject(s): Atmospheric sciences
Persistent Link to This Record:
Host Institution: FSU

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Li, J. (2019). Classification of Rain Clouds Based on the Relationship between Microwave Emission and Scattering Signals. Retrieved from