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Improving Satellite-Based Snowfall Estimation

Title: Improving Satellite-Based Snowfall Estimation: A New Method for Classifying Precipitation Phase and Estimating Snowfall Rate.
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Name(s): Sims, Elizabeth M., author
Liu, Guosheng, (Professor of Earth, Ocean and Atmospheric Science), professor directing dissertation
Meyer-Baese, Anke, university representative
Bourassa, Mark Allan, 1962-, committee member
Cai, Ming, 1957-, committee member
Sura, Philip, 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
Doctoral Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (115 pages)
Language(s): English
Abstract/Description: In order to study the impact of climate change on the Earth's hydrologic cycle, global information about snowfall is needed. To achieve global measurements of snowfall over both land and ocean, satellites are necessary. While satellites provide the best option for making measurements on a global scale, the task of estimating snowfall rate from these measurements is a complex problem. Satellite-based radar, for example, measures effective radar reflectivity, Ze, which can be converted to snowfall rate, S, via a Ze-S relation. Choosing the appropriate Ze-S relation to apply is a complicated problem, however, because quantities such as particle shape, size distribution, and terminal velocity are often unknown, and these quantities directly affect the Ze-S relation. Additionally, it is important to correctly classify the phase of precipitation. A misclassification can result in order-of-magnitude errors in the estimated precipitation rate. Using global ground-based observations over multiple years, the influence of different geophysical parameters on precipitation phase is investigated, with the goal of obtaining an improved method for determining precipitation phase. The parameters studied are near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover type. To combine the effects of temperature and moisture, wet-bulb temperature, instead of air temperature, is used as a key parameter for separating solid and liquid precipitation. Results show that in addition to wet-bulb temperature, vertical temperature lapse rate also affects the precipitation phase. For example, at a near-surface wet-bulb temperature of 0°C, a lapse rate of 6°C km-1 results in an 86 percent conditional probability of solid precipitation, while a lapse rate of -2°C km-1 results in a 45 percent probability. For near-surface wet-bulb temperatures less than 0°C, skin temperature affects precipitation phase, although the effect appears to be minor. Results also show that surface pressure appears to influence precipitation phase in some cases, however, this dependence is not clear on a global scale. Land cover type does not appear to affect precipitation phase. Based on these findings, a parameterization scheme has been developed that accepts available meteorological data as input, and returns the conditional probability of solid precipitation. Ze-S relations for various particle shapes, size distributions, and terminal velocities have been developed as part of this research. These Ze-S relations have been applied to radar reflectivity data from the CloudSat Cloud Profiling Radar to calculate the annual mean snowfall rate. The calculated snowfall rates are then compared to surface observations of snowfall. An effort to determine which particle shape best represents the type of snow falling in various locations across the United States has been made. An optimized Ze-S relation has been developed, which combines multiple Ze-S relations in order to minimize error when compared to the surface snowfall observations. Additionally, the resulting surface snowfall rate is compared with the CloudSat standard product for snowfall rate.
Identifier: FSU_2017SP_Sims_fsu_0071E_13720 (IID)
Submitted Note: A Dissertation submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the Doctor of Philosophy.
Degree Awarded: Spring Semester 2017.
Date of Defense: March 31, 2017.
Keywords: Atmospheric Science, Hydrology, Meteorology, Remote Sensing, Snowfall
Bibliography Note: Includes bibliographical references.
Advisory Committee: Guosheng Liu, Professor Directing Dissertation; Anke Meyer-Baese, University Representative; Mark A. Bourassa, Committee Member; Ming Cai, Committee Member; Philip G. Sura, Committee Member.
Subject(s): Atmospheric sciences
Meteorology
Remote sensing
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_2017SP_Sims_fsu_0071E_13720
Owner Institution: FSU

Choose the citation style.
Sims, E. M. (2017). Improving Satellite-Based Snowfall Estimation: A New Method for Classifying Precipitation Phase and Estimating Snowfall Rate. Retrieved from http://purl.flvc.org/fsu/fd/FSU_2017SP_Sims_fsu_0071E_13720