ORCID
https://orcid.org/0000-0001-8136-3812
Date of Award
Spring 2025
Language
English
Embargo Period
5-8-2025
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Atmospheric and Environmental Sciences
Program
Atmospheric Science
First Advisor
Robert Fovell
Keywords
HRRR, Verification, Numerical Weather Prediction
Subject Categories
Atmospheric Sciences | Meteorology
Abstract
NOAA’s High-Resolution Rapid Refresh (HRRR) is frequently used by forecasters for short term predictions and although the model performs well in many metrics, previous study (Fovell and Gallagher 2020) identified temperature and windspeed biases within the boundary layer using radiosonde and surface observations. The current work uses a forecast drift metric to study how model forecasts evolve with time and distance to radiosonde sites. The metric is calculated by subtracting model analysis from forecasts at the same valid time, creating a proxy for bias. HRRR data on native model levels are obtained from the Google Cloud and Amazon Web Services archives and used to create a multiple month dataset of HRRR drift files for the 00 , 06, 12, and 18 UTC cycles for forecasts with leads out to f24 and initialization at 00 to 23 UTC for analysis. These are analyzed to determine how forecast fields drift with increasing forecast time, identifying opportunities for further model improvements.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Garmong, Sylvia M., "Using the High Resolution Rapid Refresh to Validate a Novel Forecast Methodology" (2025). Electronic Theses & Dissertations (2024 - present). 220.
https://scholarsarchive.library.albany.edu/etd/220
