Earth and Atmospheric Sciences, Department of


First Advisor

Clinton Rowe

Date of this Version



Cutting, G. M., 2022: Impacts of Physical Parameterization Schemes and Soil Moisture Initialization on Boundary Layer Evolution in the Weather Research and Forecasting (WRF) Model. M.S. thesis, Dept. of Earth and Atmospheric Sciences, University of Nebraska-Lincoln, 69 pp.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Earth and Atmospheric Sciences, Under the Supervision of Professor Clinton Rowe. Lincoln, Nebraska: August 2022

Copyright © 2022 Grace Cutting


Numerical weather prediction (NWP) models have become a necessary addition to the atmospheric research community over the last several decades, and atmospheric modeling has been used internationally for numerous operational and research purposes. NWP models contain a vast number of combinations of physical and dynamical parameterization schemes; however, they are not always accurate in forecasting weather phenomena at a particular location, as different combinations of parameterization schemes represent differing conditions. Weather Research and Forecasting (WRF) model simulations were run to explore which of the commonly used planetary boundary layer (PBL) schemes best represented upper-air data (as well as PBL evolution) over northeastern Colorado, southeastern Wyoming, southwestern Nebraska, and northwestern Kansas. Additionally, errors in soil moisture initialization were investigated to determine if there was an impact on boundary layer evolution. Based on model soundings, the Grenier-Bretherton-McCaa (GBM) scheme was the most representative of this region in terms of the overall PBL structure, and there was no evidence to suggest that errors in soil moisture initialization impacted boundary layer evolution, but rather, the choice in surface-layer scheme tended to influence the modeled boundary layer when paired with specific PBL schemes.

Advisor: Clinton Rowe

FIG4-SKEWT-LBF.png (7180 kB)
Figure 4

FIG5-SKEWT-DNR.png (5607 kB)
Figure 5