Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Abstract

The relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members' ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability among the ensemble members is largest near the ABL top during the daytime, apparently because of uncertainty in ABL depth. This persists to the second day of the simulation for the 4-member diagnosis mini-ensemble, which varies only the ABL depth, but for the 12-member physics ensemble the concentration variability is large throughout the daytime ABL. This suggests that the increased within-ABL concentration variability on the second day is due to larger differences among the ensemble members' predicted meteorological conditions rather than being solely due to differences in the ABL depth diagnosis methods. This work demonstrates new analysis methods for the relationship between ABL depth and plume depth within an ensemble framework and provides motivation for directly including ABL depth uncertainty from a meteorological model into an ATD model.

Combining vLAPS and Nudging Data Assimilation

“…The Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) V3.6.1 [16] was used with 56 vertical layers over a 1 km-spaced 801 × 801 horizontal grid centered over Oklahoma (Figure 1). The Mellor-Yamada-Janjić scheme (MYJ; [24]) was used to parameterize the atmospheric boundary layer (ABL) with the background turbulent kinetic energy and atmospheric boundary layer depth calculation altered as in Lee et al [25] and Reen et al [26]. The Thompson microphysics parameterization [27], the Rapid Radiative Transfer Model longwave scheme [28], the Dudhia shortwave scheme [29], and the Noah land surface model [30] were used.…”

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

“…This combination of physics parameterizations is very similar to that used in Reen et al [24] which applied a 9 km WRF domain centered over southern California; the only difference is the use of the Thompson microphysics scheme here. All of the physics parameterizations chosen for this study have been widely used in other studies, with the exception of the modifications to the MYJ scheme which have however been applied previously over this region [24] and in other studies [30,31].…”

Mitigating Excessive Drying From the Use of Observations in Mesoscale Modeling

“…This parameterization predicts turbulent kinetic energy (TKE) and is a Mellor-Yamada Level 2.5 turbulence closure model. As in Lee et al (2012) and Reen et al (2013), the background TKE is decreased to better simulate conditions with low-TKE and the atmospheric boundary layer (ABL) depth diagnosis is altered. In preliminary experiments for this study, the standard MYJ scheme resulted in noisy TKE fields and thus noisy ABL depth fields over the water.…”