Contributions of Mixed Physics versus Perturbed Initial/Lateral Boundary Conditions to Ensemble-Based Precipitation Forecast Skill

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Abstract An experiment is described that is designed to examine the contributions of model, initial condition (IC), and lateral boundary condition (LBC) errors to the spread and skill of precipitation forecasts from two regional eight-member 15-km grid-spacing Weather Research and Forecasting (WRF) ensembles covering a 1575 km × 1800 km domain. It is widely recognized that a skillful ensemble [i.e., an ensemble with a probability distribution function (PDF) that generates forecast probabilities with high resolution and reliability] should account for both error sources. Previous work suggests that model errors make a larger contribution than IC and LBC errors to forecast uncertainty in the short range before synoptic-scale error growth becomes nonlinear. However, in a regional model with unperturbed LBCs, the infiltration of the lateral boundaries will negate increasing spread. To obtain a better understanding of the contributions to the forecast errors in precipitation and to examine the window of forecast lead time before unperturbed ICs and LBCs begin to cause degradation in ensemble forecast skill, the “perfect model” assumption is made in an ensemble that uses perturbed ICs and LBCs (PILB ensemble), and the “perfect analysis” assumption is made in another ensemble that uses mixed physics–dynamic cores (MP ensemble), thus isolating the error contributions. For the domain and time period used in this study, unperturbed ICs and LBCs in the MP ensemble begin to negate increasing spread around forecast hour 24, and ensemble forecast skill as measured by relative operating characteristic curves (ROC scores) becomes lower in the MP ensemble than in the PILB ensemble, with statistical significance beginning after forecast hour 69. However, degradation in forecast skill in the MP ensemble relative to the PILB ensemble is not observed in an analysis of deterministic forecasts calculated from each ensemble using the probability matching method. Both ensembles were found to lack statistical consistency (i.e., to be underdispersive), with the PILB ensemble (MP ensemble) exhibiting more (less) statistical consistency with respect to forecast lead time. Spread ratios in the PILB ensemble are greater than those in the MP ensemble at all forecast lead times and thresholds examined; however, ensemble variance in the MP ensemble is greater than that in the PILB ensemble during the first 24 h of the forecast. This discrepancy in spread measures likely results from greater bias in the MP ensemble leading to an increase in ensemble variance and decrease in spread ratio relative to the PILB ensemble.