Probabilistic aspects of meteorological and ozone regional ensemble forecasts

Abstract

[1] This study investigates whether probabilistic ozone forecasts from an ensemble can be made with skill: i.e., high verification resolution and reliability. Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British Columbia, Canada, for the 5-day period 11-15 August 2004 and compared with 1-hour averaged measurements of ozone concentrations at five stations. The forecasts were obtained by driving the Community Multiscale Air Quality Model (CMAQ) model with four meteorological forecasts and seven emission scenarios: a control run, ±50% NO x , ±50% volatile organic compounds (VOC), and ±50% NO x combined with VOC. Probabilistic forecast quality is verified using relative operating characteristic curves, Talagrand diagrams, and a new reliability index. Results show that both meteorology and emission perturbations are needed to have a skillful probabilistic forecast system: the meteorology perturbation is important to capture the ozone temporal and spatial distribution and the emission perturbation is needed to span the range of ozone concentration magnitudes. Emission perturbations are more important than meteorology perturbations for capturing the likelihood of high ozone concentrations. Perturbations involving NO x resulted in a more skillful probabilistic forecast for the episode analyzed, and therefore the 50% perturbation values appear to span much of the emission uncertainty for this case. All of the ensembles analyzed show a high ozone concentration bias in the Talagrand diagrams, even when the biases from the unperturbed emissions forecasts are removed from all ensemble members. This result indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its interaction with input from particular meteorological models.

A refinement of the emission data for Kola Peninsula based on inverse dispersion modelling

“…The spread between the individual models then indicates the predictability of the episode, its stochastic features, and the potential range of the uncertainties in the results of the simulations. More sophisticated approaches are under construction, aiming at the optimal selection and combination of the ensemble members and at softening or lifting some of the underlying assumptions concerning the relation between the ensemble and the actual probability distribution (Galmarini et al, 2004c;Mallet and Sportisse, 2006;Riccio et al, 2007;Delle Monache et al, 2006).…”

Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds

“…where f j is the observed relative frequency of rank j (Delle Monache et al 2006;Berrocal et al 2007). Formal tests for uniformity can be developed; they require care in interpretation and have been used in economic as well as meteorological applications (Hamill 2001;Clements 2005).…”

A Comparison Study of Ensemble Approach Using WRF/CMAQ Model - The High PM10Episode in Busan

“…Jang et al (2014)에서는 기상 자료의 불확실성 (Solazzo et al, 2012;Huijnen et al, 2010;Monache et al, 2006;Pagowski et al, 2006;McKeen et al, 2005;Monache and Stull, 2003 (28 Feb~06 Mar, 2013;blue). The azimuthal position gives the correlation, while the radial distance from the origin is proportional to the normalized standard deviation.…”