Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item DEVELOPMENT AND APPLICATION OF PROPINQUITY MODELING FRAMEWORK FOR IDENTIFICATION AND ANALYSIS OF EXTREME EVENT PATTERNS(2024) kholodovsky, vitaly; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damage. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: construction of essential field quantities, dimension reduction, spatial domain mapping, time series clustering, and threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale, well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heatwaves, especially during the summer months. Additionally, we compare empirical and statistical probabilities and intensities obtained through the Conventional Location Specific methods, which are deficient in geometric interconnectivity between individual spatial pixels and independent in time, with a new Propinquity modeling framework. We integrate the Spatio-Temporal Threshold Clustering algorithm and the conditional semi-parametric Heffernan and Tawn (2004) model into the Propinquity modeling framework to separate classes of models that can calculate process level dependence of large-scale extreme processes, primarily through the overall extreme spatial field. Our findings reveal significant differences between Propinquity and Conventional Location Specific methods, in both empirical and statistical approaches in shape and trend direction. We also find that the process of aggregating model results without considering interconnectivity between individual grid cells for trend construction can lead to significant variations in the overall trend pattern and direction compared with models that do account for interconnectivity. Based on these results, we recommend avoiding such practices and instead adopting the Propinquity modeling framework or other spatial EVA models that take into account the interconnectivity between individual grid cells. Our aim for the final application is to establish a connection between extreme essential field quantity intensity fields and large-scale circulation patterns. However, the Conventional Location Specific Threshold methods are not appropriate for this purpose as they are memoryless in time and not able to identify individual extreme episodes. To overcome this, we developed the Feature Finding Decomposition algorithm and used it in combination with the Propinquity modeling framework. The algorithm consists of the following three steps: feature finding, image decomposition, and large-scale circulation patterns connection. Our findings suggest that the Western Pacific Index, particularly its 5th percentile and 5th mode of decomposition, is the most significant teleconnection pattern that explains the variation in the trend pattern of the largest feature intensity.Item IMPROVING U.S. EXTREME PRECIPITATION PREDICTION AND PROCESS UNDERSTANDING USING A MESOSCALE CLIMATE MODEL MULTI-PHYSICS ENSEMBLE APPROACH(2019) sun, chao; Liang, Xin-zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite many recent improvements, climate models continue to poorly simulate extreme precipitation. I attempted to improve prediction of extreme precipitation, focusing on daily 95th percentile (P95) events, and to better understand the source of model biases in three ways: 1) determine which physics processes P95 is most sensitive to and which parameterization schemes best represent these processes; 2) understand the underlying mechanisms through which these processes impact P95; and 3) maximize advantages from the ensemble of the best performing models. First, to determine the sensitive processes affecting P95, I tested a 25-member ensemble of different physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF) for 36-yr historical U.S. simulations. Of these, P95 simulation was most sensitive to cumulus parameterization. Overall, the ensemble cumulus parameterization best represented P95 seasonal mean spatial patterns and interannual variations, while one traditional cumulus scheme generally overestimated P95 and the other three severely underestimated P95, especially over the Gulf States (GS) and the Central-Midwest States (CM) in convection-dominated seasons. Second, I built structural equation models (SEMs) to identify the underlying processes through which cumulus parameterization affects precipitation. I discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings. The relative importance of these factors varied significantly by season and region. For example, water supply is the dominant factor for P95 in CM, but its effect reversed from positive in summer to negative in winter due to changes in the prevailing precipitation system. In contrast, the predominant factors affecting P95 in GS were cloud forcing in summer, but surface forcing in winter. Since the choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, it determined CWRF’s ability to simulate extreme precipitation. Third, I improved P95 prediction by developing an optimized multi-model ensemble based on the Bayesian Model Averaging (BMA) approach. BMA is a model-selection method that weights ensemble members to create an optimal composite. However, many BMA methods rely on maximum likelihood estimation and thus may be flawed when the true solution is not among the ensemble, as is the case in extreme precipitation. To resolve this issue, I adapted three BMA variations to fit the needs of extreme precipitation problems. These methods significantly improved performance compared to both the ensemble mean and the single best model and provided a more reliable confidence interval. My work shows that to improve extreme precipitation simulation, a better understanding of physics processes, especially cumulus processes, is critical. For this, I applied the SEM framework, for the first time in the climate community, to uncover the underlying physical mechanisms essential to regional extreme precipitation predictions. Furthermore, I adapted new BMA methods into extreme precipitation ensembles to maximize the benefits from the most physically advanced models. These advances may help improve the prediction of extreme precipitation occurrences and future changes, one of the most difficult modeling challenges and one with huge socioeconomic significance.Item The Frequency Distribution of Daily Precipitation over the United States(2008) Becker, Emily Jones; Berbery, Ernesto Hugo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study examines the seasonal frequency distribution of daily precipitation and related variables over the United States using the North American Regional Reanalysis. Regions where the seasonal mean precipitation is dominated by heavy and extreme daily events or by more consistent lighter events are identified. The distributions are related to the variability of the vertically integrated moisture flux convergence (MFC) and precipitable water. The modulation of daily precipitation by ENSO and the Madden-Julian Oscillation (MJO) during winter is investigated. Assuming that the frequency of daily precipitation can be approximated by a gamma distribution, the scale and shape parameters are useful proxies to estimate the observed frequency distribution of precipitation. During winter, most areas of the country with high mean precipitation have a strong contribution from extreme events, particularly along the West and Gulf Coasts. During summer, the wettest areas of the country are Florida, where the mean precipitation is dominated by more-frequent light and moderate rainfall days, and the central Plains, dominated by variable rains and extreme events. Throughout the year, the MFC mean and scale parameter patterns strongly resemble those of precipitation, and areas with more heavy and extreme precipitation have stronger MFC daily values. These results suggest that the frequency distribution of MFC can be used as a proxy for the frequency distribution of modeled forecast precipitation. Changes in the winter total precipitation between the phases of ENSO are largely attributable to changes in the heavy and extreme events. Areas showing increased mean precipitation during the warm phase show an even greater increase in extremes. Similar to precipitation, strong values of MFC are more sensitive to ENSO phase than is the mean MFC. While the ENSO variability of the frequency distribution of MFC shows a strong relationship to that of precipitation, the variability of precipitable water does not. MJO modulation of winter daily precipitation over the central U.S. occurs primarily during MJO Phases 5 and 6, when MJO-related enhanced convection is located in the western Pacific. During these phases, the winter storm track is enhanced, and positive MFC anomalies are present in the central U.S.