Discipline: Mathematics and Statistics
Mikol Forney - North Carolina A&T State University
The study of weather is a very broad subject with many directions and topics of research. One of the oldest studies within weather that we will be diving into is the study on the prediction of precipitation. When a tropical cyclone passes over a mesoscale mountain range, it usually induces heavy rainfall. The quantitative prediction of precipitation is difficult in general because of the complex mechanism in the weather system and the numerous rainfall factors. In particular, this study focuses on the precipitation associated with Hurricane Isabel (2003) passing over Appalachian Mountains. The results of this study shall help forecasters determine when and where to expect heavy rainfall. From the observation data, previous study found that in most cases there were more rains inside the mountains than the outside with no mountains. However, there were cases when there was not much rainfall inside the mountain. Also, within the mountain, there were places receiving more rain than the others. Further study discovered that there were seven major ingredients contributing to heavy rainfall. These are the heavy rainfall ingredients: potential instability, precipitation efficiency, conditional instability, relative humidity, storm propagation speed, mountain slope, and orographically-forced upward motion. The selection of the heavy rainfall ingredients was done mainly by physics analysis. This study, the first time, utilizes statistical models to investigate the effects of the heavy rainfall ingredients over the rainfall amount. We use the simulation data from the Weather Research and Forecasting (WRF) model, which is a numerical weather prediction package, for Hurricane Isabel (2003). Using multiple linear regression and a series of partial F-tests, we relate these aspects with precipitation to find out the contributions amongst these seven ingredients. Since there are 10 different times in the data, we include the times as the categorical variables. The results from the regression analysis with various times as categorical variables show that the most significant ingredient is the conditional instability, followed by mountain slope. The detailed contribution from each ingredient is shown in the full presentation. The effect of the mountain slope is not as significant in the regression analysis with time as categorical variables. Further study at chosen times reveals the significance of the mountain slope. These findings confirm the significance of the seven heavy rainfall ingredients. We further give out the rank in the contributions of the ingredients. This helps the quantitative prediction for precipitation. Furthermore, the statistical techniques in this study can be employed to investigate a broader range of rainfall ingredients in various weather systems. This study was done for one particular area and only seven ingredients, the contribution and amounts may differ for various locations and incidents.Not Submitted
Funder Acknowledgement(s): This research was conducted as a part of NC A&T SU Summer Data Science and Analytics STEM Education Research Program and funded by the National Science Foundation (NSF) under Grant HRD#1719498.
Faculty Advisor: Liping Liu, email@example.com
Role: I continued the research of a previous A&T student (The Impacts of South Central Appalachian Topography on the Predictability of Hurricane Isabel's Heavy Rainfall: A High Resolution Ensemble Simulation), where in his chapter 7 he related the 7 ingredients with rainfall variability using statistical analysis (Guy, 2016). I related these same 7 heavy rainfall ingredients with precipitation in order to find the contributions. I did not work with anyone other than my advisor on this research.