Quantifying evaporation in the lower atmosphere using machine driven curve fitting to parameterize drop size distributions
Abstract
Evaporation of hydrometeors in the atmosphere is a process by which liquid water becomes water vapor. Consequences of this process can affect quantitative precipitation forecasts, convective downdrafts quantification, flooding forecasts, and many other forecasting parameters. Accurate and precise forecast modeling often misrepresents this microscale process due to the multiple feedbacks involved, and under- or mis- quantified parameters. Resolving parameters such as the drop size distribution and statistical representation will help to rectify these inadequacies. In this study, multiple observation instruments are used to observe how rain evolves in a dry atmosphere. Instruments include weather balloon radiosondes, laser disdrometers and a vertically pointing micro-rain radar (MRR). These data are then processed using a multitude of modeling methods to best quantify evaporation rates. Several case studies were conducted with the goal of observing and modeling evaporation in a dry layer. Using the MRR and disdrometer data, drop size distributions were observed from the base of the cloud layer to the surface. Calculated liquid water content was used as a parameter to compare the modeled change in water content to the MRR and disdrometer observations. Through this process, a newly developed drop size distribution parameterization using linear regression modeling and a Gaussian distribution mixture was implemented and was shown to be a better method, capturing the larger drops in the distribution. The Gaussian mixture also demonstrated an accurate parameterization in the evaporation quantification when vertical motions were accurately represented. However, accurately quantifying vertical motions using balloon data was problematic in the cases presented and will need to be the subject for future interrogation.
Degree
M.S.
Thesis Department
Rights
OpenAccess.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. Copyright held by author.