An Assessment of Warm Season Quantitative Precipitation Forecasts from the High Resolution Rapid Refresh (HRRR) Model
Janice Bytheway
Colorado State University
Friday, Oct 14, 2016, 11:00 am
DSRC Room 2A305
Abstract
The High Resolution Rapid Refresh (HRRR) model is a 3km convection-allowing forecast model featuring diabatic assimilation of radar reflectivity running operationally in the US. With the expectation that radar assimilation will lead to skillful precipitation forecasts and decreased model spin-up time, a features-based assessment of quantitative precipitation forecasts (QPFs) from the HRRR is performed. The performance of HRRR QPFs is examined, with an emphasis on evaluating model skill through the forecast period and inferring links between forecast quality and model processes. Results indicate a tendency for the model to produce intense rainfall in concentrated cores in the first forecast hour, with improvement in skill over the first 2-3 hours of the forecast period. These results lead to further investigation of the model’s performance in the first forecast hour using products from the Global Precipitation Measurement (GPM) core satellite. In addition to assessing QPF skill, GPM products were also used to evaluate the treatment of hydrometeors in precipitating features. Comparisons between the model output and observations indicate a tendency for the model to over-produce hydrometeors classified as snow, and possible explanations for this phenomenon are discussed. Opportunities for continued exploration into the relationship between QPF skill and model processes will also be presented.
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Seminar Contact: madeline.sturgill@noaa.gov