Physical Sciences and Mathematics Commons^™

Ensemble Data Fitting For Bathymetric Models Informed By Nominal Data, Samantha Zambo

Dissertations

Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension.

In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge …

Analysis Of Uncertainty In Hydrometeorological Ensemble Forecasts, Carolien N. Mossel

Dissertations and Theses

Ensemble hydrometeorological forecasting has great potential for improving flood predictions and use in water management systems, however, the amount of data used and created with an ensemble forecast requires a careful and intentional approach to understand how useful and skillful the forecast is. The NOAA National Water Model (NWM) was run using downscaled NOAA Global Ensemble Forecast System (GEFS) meteorological forcings for the 2016-2017 wet season (October-March) in California to create an 11-member hydrologic forecast ensemble. To evaluate the performance of these ensemble forecasts, we chose to study streamflow sites within Sonoma County, California, a rain-dominated region which includes the …

Identifying The Microphysical Sensitivities Of Mesoscale And Synoptic Precipitation Using An Ensemble Framework, Lauriana Gaudet

Legacy Theses & Dissertations (2009 - 2024)

Through ensemble sensitivity analysis, this dissertation aims to identify the amount of forecast uncertainty that stems from the representation of mixed-phase cloud microphysics within the Weather Research and Forecasting Model (WRF). The first research thrust focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the Adaptive Habit Microphysics Model (AHM) influences the lake-effect storm that occurred during Intensive Operating Period 4 (IOP4) of the Ontario Winter Lake Effect Systems (OWLeS) Field Campaign. This localized snowstorm produced total liquid-equivalent precipitation amounts up to 17.92 mm during a 16-hour time period, providing a natural laboratory …

Process-Based Evaluation Of Stochastic Perturbed Parameterization Tendencies On Ensemble Forecasts Of Heavy Rainfall Events, Kevin Michael Lupo

Legacy Theses & Dissertations (2009 - 2024)

Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly utilized method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on how stochastic methods influence ensemble skill, relatively less attention is given to the processes by which these schemes lead to forecast variability. In this vein, this dissertation examines the physical processes by which the application of SPPT and iSPPT to the microphysics, planetary boundary layer (PBL), and radiation parameterization schemes yields rainfall forecast variability. These …