"Using the High Resolution Rapid Refresh to Validate a Novel Forecast M" by Sylvia M. Garmong

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

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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