Sensitivity of WRF Precipitation Simulations to Microphysics and Cumulus Parameterizations: A Case Study of an Extreme Rainfall Event over Alabama

Extreme precipitation events in the southeastern United States pose significant challenges for numerical weather prediction because subgrid-scale processes strongly influence rainfall intensity, spatial distribution, and timing. This study assesses the sensitivity of Weather Research and Forecasting (WRF) Model v4.7 precipitation simulations during an intense rainfall episode over Alabama from 27 April to 3 May 2014. Stage IV observations indicated localized precipitation totals exceeding 200 mm, with a domain-mean accumulation of 42.48 mm. A 12-member physics ensemble was created by pairing two double-moment microphysics schemes, WDM6 and Morrison, with six cumulus parameterization schemes: Grell–Devenyi (GD), Grell–Freitas (GF), Betts–Miller–Janjic (BMJ), Kain–Fritsch (KF), KF-CuP, and Tiedtke. Model outputs were regridded to the 4 km Stage IV analysis grid and evaluated using conventional error statistics alongside spatial verification metrics, such as the Fractions Skill Score (FSS), spatial correlation, and temporal diagnostics. This multifaceted evaluation mitigated sensitivity to minor spatial displacement errors, enabling a more nuanced assessment of forecast performance. In addition, two simulations were conducted without cumulus parameterization as a bounding check on this modeling choice at the 4 km, gray-zone grid spacing. Within this experimental design, the choice of cumulus parameterization was the largest source of spread in precipitation forecast skill, strongly affecting storm-total rainfall, spatial distribution, and peak precipitation timing. Configurations using the KF family of schemes typically provided the most accurate representation of organized heavy rainfall characteristics, whereas the BMJ scheme notably underestimated both precipitation intensity and areal coverage. Although the effects of microphysics were secondary, distinct patterns emerged: the Morrison scheme generally improved volumetric precipitation accuracy, whereas WDM6 concentrated extreme rainfall into more localized cores when paired with aggressively triggered cumulus schemes. Spatial verification techniques uncovered performance differences that traditional grid-point statistics did not fully capture, allowing us to identify the most effective physics configuration among those tested. However, overall forecast skill remained limited; none of the configurations met the commonly accepted useful-skill threshold (FSS ≥ 0.5) for the ≥200 mm precipitation category at any neighborhood scale analyzed. This underscores the ongoing challenges in accurately representing the most intense rainfall cores. Because this analysis is based on a single extreme event, caution is warranted when extrapolating these findings. Further evaluations across a wider array of storm environments are essential to substantiate the identified model sensitivity.