Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

• Published in: Journal of applied meteorology and climatology Vol. 56; no. 8; p. 2317
• Main Authors: Griffin, Sarah M, Otkin, Jason A, Rozoff, Christopher M, Sieglaff, Justin M, Cronce, Lee M, Alexander, Curtis R, Jensen, Tara L, Wolff, Jamie K
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01-08-2017
• Subjects: Accuracy; Brightness; Brightness temperature; Cloud computing; Clouds; Computer simulation; Diagnostic systems; Displacement; Diurnal cycle; Diurnal variations; Evaluation; Forecast accuracy; GOES satellites; High resolution; Identification; Identification methods; Inspection; Mathematical models; Meteorological satellites; Rain; Resolution; Satellites; Seasons; Studies; Weather forecasting
• ISSN: 1558-8424; 1558-8432
• DOI: 10.1175/JAMC-D-17-0004.1">

In this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-μm brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.