Date of Award

1-1-2023

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (ix, 189 pages) : illustrations (some color)

Dissertation/Thesis Chair

Justin R Minder

Committee Members

Kara J Sulia, Ryan D Torn, Robert G Fovell, James Steenburgh

Keywords

ensemble forecasting, lake-effect snow, microphysics, orographic precipitation, planetary boundary layer, stochastic parameter perturbations, Weather forecasting

Subject Categories

Atmospheric Sciences

Abstract

Accurate winter precipitation forecasts are difficult due to factors influencing precipitation amounts and precipitation types including numerical weather prediction biases. In particular, large forecast errors can arise due to uncertainties in parameterized processes, especially those related to microphysics and turbulence. Many winter weather impacts are due to mesoscale precipitation features that are better represented in convection-permitting model forecasts. One way to account for model physics uncertainty is to design convection-permitting ensembles using stochastic physics methods. For example, stochastic parameter perturbation (SPP) varies parameters within individual schemes. SPP methods can be combined with varied initial and boundary conditions (ICs/BCs) to represent synoptic-scale uncertainty in limited-area ensembles. In this dissertation, I evaluate and improve the utility of SPP in microphysics (MP), planetary boundary layer (PBL), and surface layer (SL) schemes to better represent mesoscale uncertainty in ensemble forecasts of cool-season events.

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