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 Title: Parameterizing PSD assumptions for remote sensing algorithms Author(s): Harnos, Kirstin Director of Research: Nesbitt, Stephen W. Doctoral Committee Chair(s): Nesbitt, Stephen W. Doctoral Committee Member(s): Rauber, Robert; Walsh, John E.; Kristovich, David Department / Program: Atmospheric Sciences Discipline: Atmospheric Sciences Degree Granting Institution: University of Illinois at Urbana-Champaign Degree: Ph.D. Genre: Dissertation Subject(s): Global Precipitation Mission Algorithm Snowfall Assumptions Abstract: The Global Precipitation Measurement (GPM) mission Core satellite is the next generation of spacebased precipitation monitoring. Upgrading the ability to measure light precipitation and snowfall from a nearly global perspective. With these new capabilities comes new levels of uncertainty within the retrieval algorithms, specifically the assumptions associated with snowfall particle size distributions (PSD). In support of the GPM mission, the Ground Validation (GPM-GV) program sponsored field campaigns to collect a comprehensive precipitation dataset utilizing airborne, ground-based, and simulated data for validation and improvement to the GPM retrieval algorithms. As part of GPM-GV, two field campaigns collected data focusing in the high-latitudes (poleward of 45$^{\circ}$N): the Light Precipitation Validation Experiment (LPVEx) in Southern Finland from September to December 2010 and the GPM Cold-Season Precipitation Experiment (GCPEx) in Southern Ontario from January to March 2012. GCPEx utilized aircraft and ground instrumentation to sample snowfall characteristics in Ontario, Canada from January to March 2012. In-situ measurements from the University of North Dakota Citation aircraft and 2-D video disdrometers (2DVD) represent a large dataset of particle size distributions (PSD) from which statistically independent relationships between PSD parameters can be determined utilizing a new framework. This framework, introduced in \cite{williams2014}, determines relationships from the mass spectrum of the PSD and has been shown previously to reduce normalized bias in retrieved rainfall rates. Once PSD relationships are determined for snowfall from GCPEx, the variability is examined using measured environmental parameters of temperature, liquid and ice water content, and relative humidity. While temperature and water content show organization within the data distributions, application of the environmental influence on the relationship is unlikely to be useful within the GPM algorithm. Case studies of the 21 September and 20 October 2010 IOPs from LPVEx are performed using in-situ aircraft measurements, ground-based 2D video disdrometers (2DVD), and high-resolution simulations using the Weather Research and Forecasting (WRF) model. WRF simulations for each case use two different microphysical parameterizations: the Goddard 6-class scheme and the WRF single moment 6-class scheme. Simulations of the case studies includes construction of vertical columns using WRF output for comparison to aircraft spirals. Comparisons between observed and WRF simulated data within the vertical columns shows a WRF environment similar to what was sampled by aircraft in terms of temperature, relative humidity, and hydrometeor water content. Particle size distribution assumptions within the WRF microphysical schemes are compared to exponential size distributions from both the aircraft and surface distributed 2DVD measurements. Results shows large differences, some exceeding an order of magnitude, between assumed and measured particle size distribution characteristics, and particle fall speeds. This project addresses three objectives in support of the GPM satellite retrieval algorithms. A framework is adapted to characterize ice phase PSDs from GCPEx in a statistically independent manner. Variability is then explored within the PSDs using environmental measurements. Finally, case studies from LPVEx focus on whether or not current microphysical assumptions within cloud-resolving model simulations are representative of high-latitude light precipitation. Issue Date: 2015-02-24 Type: Thesis URI: http://hdl.handle.net/2142/78711 Rights Information: Copyright 2015 Kirstin Joy Harnos Date Available in IDEALS: 2015-07-22 Date Deposited: May 2015
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