A Comparison of 36–60-h Precipitation Forecasts from Convection-Allowing and Convection-Parameterizing Ensembles

Autor: Ming Xue, Eswar R. Iyer, Adam J. Clark, Fanyou Kong
Rok vydání: 2016
Předmět:
Zdroj: Weather and Forecasting. 31:647-661
ISSN: 1520-0434
0882-8156
Popis: Previous studies examining convection-allowing models (CAMs), as well as NOAA/Hazardous Weather Testbed Spring Forecasting Experiments (SFEs) have typically emphasized “day 1” (12–36 h) forecast guidance. These studies find a distinct advantage in CAMs relative to models that parameterize convection, especially for fields strongly tied to convection like precipitation. During the 2014 SFE, “day 2” (36–60 h) forecast products from a CAM ensemble provided by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma were examined. Quantitative precipitation forecasts (QPFs) from the CAPS ensemble, known as the Storm Scale Ensemble Forecast (SSEF) system, are compared to NCEP’s operational Short Range Ensemble Forecast (SREF) system, which provides lateral boundary conditions for the SSEF, to see if the CAM ensemble outperforms the SREF through forecast hours 36–60. Equitable threat scores (ETSs) were computed for precipitation thresholds ranging from 0.10 to 0.75 in. for each SSEF and SREF member, as well as ensemble means, for 3-h accumulation periods. The ETS difference between the SSEF and SREF peaked during hours 36–42. Probabilistic forecasts were evaluated using the area under the receiver operating characteristic curve (ROC area). The SSEF had higher values of ROC area, especially at thresholds ≥ 0.50 in. Additionally, time–longitude diagrams of diurnally averaged rainfall were constructed for each SSEF/SREF ensemble member. Spatial correlation coefficients between forecasts and observations in time–longitude space indicated that the SSEF depicted the diurnal cycle much better than the SREF, which underforecasted precipitation with a peak that had a 3-h phase lag. A minority of SREF members performed well.
Databáze: OpenAIRE