Improving Convective Permitting Model Simulations of Extreme Precipitation using Object-Based Tracking and Data Assimilation

This dissertation explores the improvement of mesoscale convective system (MCS) tracking and forecasting during severe weather episodes over the central and southern US using high-resolution numerical simulation and advanced data assimilation.In Appendix A, we evaluate the performance of a km-scale (e.g. 1.8 km) convective-permitting model (CPM) implementation of the Weather Research and Forecasting (WRF) model in simulating MCS activity during the record-flooding period over Texas from 21 to 31 May 2015. Applying satellite-based MCS tracking and multiple precipitation datasets, we show that the CPM significantly better captures MCS frequencies and associated extreme rainfalls than coarse-grained (e.g. 3 km) ensemble models. Probabilistic verification illustrates that CPM has a much better skill to predict intense convective events especially for the target area of central Texas where the hourly rainfall rates and flooding were extreme. In Appendix B, we move our attention to the North American Monsoon (NAM) that takes place in Arizona, and we present an Observing System Simulation Experiment (OSSE) framework to test the impact of assimilating GPS-derived Precipitable Water Vapor (PWV) using different densities of observation. Results from a 40-member WRF CPM ensemble are produced for two fairly different monsoon events in the year 2021, and demonstrate that assimilating bias-corrected synthetic PWV mitigates the initial moisture errors and enhances early forecast skill. The experiments find 100 km network spacing to be optimal, in terms of observation density and errors. Finally, Appendix C describes a new assimilation of high-vertical-resolution absolute humidity (AH) observations from MicroPulse Differential Absorption Lidar (MPD). These profiles are utilized in a similar OSSE configuration to improve elevation-dependent dry biases that persist in the assimilation of GPS-derived PWV and enhance moisture initialization in complex terrain. Together, these studies highlight the necessity of high-resolution ensemble model simulation and targeted observation network design, especially for arid, terrain-complex regions to enhance prediction of high-impact weather and push forward operational capabilities at convective scales.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2025

Author dc:creator

• Shohan, S M Samkeyat

Advisors dc:contributor.advisor

• Castro, Christopher L.
• Koch, Steven E.

Committee members dc:contributor.committeemember

• Arellano, Avelino F.
• Chang, Hsin-I
• Sullivan, Sylvia

Statement dc:rights

• Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10150/678341

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/678341