Improving SCIPUFF Dispersion Forecasts with NWP Ensembles

Abstract

The relationships between atmospheric transport and dispersion (AT&D) plume uncertainty and uncertainties in the transporting wind fields are investigated using the Second-Order Closure, Integrated Puff (SCIPUFF) AT&D model driven by numerical weather prediction (NWP) meteorological fields. Modeled contaminant concentrations for episode 1 of the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) are compared with recorded ground-level concentrations of the inert tracer gas C 7 F 14 . This study evaluates a Taylor-diffusion-based parameterization of dispersion uncertainty for SCIPUFF that uses Eulerian meteorological ensemble velocity statistics and a Lagrangian integral time scale as input. These values are diagnosed from NWP ensemble data. Individual simulations of the tracer release fail to reproduce some of the monitored surface concentrations of the tracer. The plumes that are predicted using the uncertainty model in SCIPUFF are broader, improving the overlap between the predicted and observed results. Augmenting the meteorological input to SCIPUFF with meteorological ensemble-uncertainty parameters therefore provides both a better estimate of the expected plume location and the relative uncertainties in the predicted concentrations than single deterministic forecasts. These results suggest that this new parameterization of NWP wind field uncertainty for dispersion may provide more sophisticated information that may benefit emergency response and decision making.

Evaluation of Lagrangian Particle Dispersion Models with Measurements from Controlled Tracer Releases

“…Although other tracers have been used for validation of transport models (e.g., Jacob et al 1997), controlled releases for which the emissions and resulting concentrations are accurately known arguably constitute the ''gold standard'' for evaluating transport models. Data from a single CAPTEX release have been used in several model-evaluation studies that used highresolution (12 and 4 km) meteorological data (Deng et al 2004;Lee et al 2009;Deng and Stauffer 2006), and both ANATEX and CAPTEX data for multiple releases were evaluated with FLEXPART by Stohl et al (1998) with 18 global meteorological fields. In the evaluation that is presented here, each LPDM is driven by hourly meteorological inputs from the Weather Research and Forecasting (WRF) model (Skamarock and Klemp 2008) with 10-km horizontal grid spacing.…”

On the systematic reduction of data complexity in multimodel atmospheric dispersion ensemble modeling

“…Within recent years, the use of model ensembles has become an important component in weather [ Bowler et al , 2008; Du et al , 2009; Houtekamer et al , 1996; Hacker et al , 2011; Stensrud et al , 2010], air quality [ McKeen et al , 2005; Pagowski et al , 2005, 2006; Pagowski and Grell , 2006; Mallet and Sportisse , 2006; Delle Monache et al , 2006a, 2006c, 2006b; Zhang et al , 2007; Vautard et al , 2009] and atmospheric dispersion predictions [ Galmarini et al , 2001; Warner et al , 2002; Draxler , 2011; Lee et al , 2009; Kolczynski et al , 2009]. Moreover, recent efforts demonstrated the superior performance of multimodel ensembles (comprising multiple runs of different numerical prediction models, which differ in the input initial and/or boundary conditions and the numerical representation of the atmosphere) in weather [ Krishnamurti et al , 2009; Bougeault et al , 2011], climate [ Krishnamurti et al , 2000] and atmospheric dispersion modeling [ Galmarini et al , 2004a; Riccio et al , 2007; Potempski et al , 2008].…”

Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

“…In our companion study (Lee et al 2009), we demonstrate that parameterization in a case study of an ensemble based on differing physics configurations for the 1983 Cross-Appalachian Tracer Experiment (CAPTEX). This parameterized approach seeks to estimate the uncertainty in dispersion within a single atmospheric and transport dispersion model run.…”