The representation of model error in ensemble prediction systems (EPSs) can be limited by the assumptions within parameterization schemes. Stochastic perturbed parameterization tendencies (SPPT) is one representation of model error that randomly perturbs parameterized physical tendencies using a spatially and temporally correlated red-noise field. This research investigates the sensitivity of ensemble rainfall forecasts produced by the Weather Research and Forecasting (WRF) Model to the configuration of SPPT and independent SPPT (iSPPT) for three meso–synoptic-scale heavy rainfall events over the United States and Taiwan, primarily focusing on the ensemble mean and standard deviation as well as forecast skill. Thirty-two 20-member ensembles, which represent a combination of eight configurations of the stochastic perturbation time scale, length scale, and amplitude scale, and four perturbed parameterization schemes, as well as an unperturbed control simulation, are examined for each event. In each case, rainfall standard deviation is most sensitive to the perturbation time scale and amplitude scale. Moreover, microphysics tendency perturbations are associated with the largest standard deviation in two of the three events, followed by perturbations to the total (nonmicrophysics), turbulent mixing, and radiation parameterized tendencies. Additionally, microphysics tendency perturbations are associated with an increase in the areal coverage of heavy rainfall compared to the control forecast, regardless of whether the control forecast over or underrepresents the observed rainfall distribution.

The formation of katabatic winds and pooling of cold air in mountain valleys impact air quality, precipitation type, and local ecosystem functions. Much is still poorly understood about the multiscale interaction of processes in a mature mixed-hardwood forest that cause the formation and evolution of cold-air pools (CAPs). Processes involved in the evolution of a CAP in the Hubbard Brook Experimental Forest valley in New Hampshire were investigated during a field campaign on 4–5 November 2015. Vertical profiles of temperature and humidity were measured along a 150-m-long tethered balloon in the center of the valley and were compared with temperature and wind observations on the surrounding slopes to identify and assess the impacts of multiscale processes on a CAP. A CAP formed rapidly during the afternoon of 4 November and attained its maximum depth of ~150 m by sunset. This maximum depth is likely a result of the topography of the valley. Warm-air advection (WAA) occurred during the second half of the night at high elevations, and warm air mixed downward into the valley. As a result, the vertical thermal gradient strengthened and static stability increased, which allowed the lowest part of the CAP to continue to radiatively cool while the upper part of the CAP was warmed and eroded by the WAA. Results suggest that the canopy acts as the primary cooling surface for air at night, which causes split katabatic flow: cold and fast flow above canopy and warmer and slower flow below canopy. Understanding these processes in sloped forests has implications for eddy covariance research and montane microclimates.

Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud-level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.