Date of Award
Fall 2024
Language
English
Embargo Period
12-15-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School/Department
Department of Atmospheric and Environmental Sciences
Program
Atmospheric Science
First Advisor
Everette Joseph
Second Advisor
Robert G. Fovell
Committee Members
Jimy Dudhia, Fangqun Yu, Scott Miller
Keywords
cloud radiative forcing (CRF), hydrometeor interactions, precipitation type (P-type)
Subject Categories
Atmospheric Sciences
Abstract
A major winter storm on March 2, 2018, posed significant challenges in predicting precipitation type (P-type) in the northeast U.S., particularly the Hudson Valley. While National Weather Service (NWS) forecasts predicted a mix of rain and snow, observations showed predominantly snow, with errors in snow depth forecasts of 8-12 inches near Albany.
This study used the Weather Research and Forecasting (WRF) model to investigate how model physics (microphysics, cumulus, boundary layer, and radiation schemes) and domain configuration influenced snow depth predictions. Results revealed significant sensitivities to cumulus and radiation schemes, as well as the vertical resolution in the middle troposphere. Observations from NWS snowfall analysis, the New York State Mesonet (NYSM), and public information statements (PNS) were used for verification.
Experiments were designed to explore how cloud-radiative forcing (CRF) and hydrometeor interactions affect snowfall and P-type. Removing or reactivating hydrometeor species (e.g., cloud water, cloud ice, snow and rain) in simulations demonstrated their varying impacts on atmospheric temperature, stability, and precipitation type. Shortwave CRF experiments showed warming near planetary boundary layer (PBL) compared to the simulation with all hydrometeors activated, leading to increased rain and reduced snow accumulation. In contrast, longwave CRF experiments caused cooling near PBL compared to the simulation with all hydrometeors activated, which promoted greater snow accumulation. Among hydrometeors, cloud water and snow have the most pronounced influence, while rain has the least impact, and cloud ice has a moderate influence.
License
This work is licensed under the University at Albany Standard Author Agreement.
Recommended Citation
Chen, Yanna, "Investigating the Uncertainties of Forecasting NE Cold Season Precipitation in Numerical Weather Prediction Models" (2024). Electronic Theses & Dissertations (2024 - present). 89.
https://scholarsarchive.library.albany.edu/etd/89
