Automatic generation of large ensembles for air quality forecasting using the Polyphemus system

摘要

This paper describes a method to automatically generate a large ensemble ofair quality simulations. Such an ensemble may be useful for quantifyinguncertainty, improving forecasts, evaluating risks, identifying processweaknesses, etc. The objective is to take into account all sources ofuncertainty: input data, physical formulation and numerical formulation. Theleading idea is to build different chemistry-transport models in the sameframework, so that the ensemble generation can be fully controlled. Largeensembles can be generated with a Monte Carlo simulations that address at thesame time the uncertainties in the input data and in the model formulation.This is achieved using the Polyphemus system, which is flexible enough tobuild various different models. The system offers a wide range of options inthe construction of a model: many physical parameterizations, severalnumerical schemes and different input data can be combined. In addition,input data can be perturbed. In this paper, some 30 alternatives areavailable for the generation of a model. For each alternative, the optionsare given a probability, based on how reliable they are supposed to be. Eachmodel of the ensemble is defined by randomly selecting one option peralternative. In order to decrease the computational load, as manycomputations as possible are shared by the models of the ensemble. As anexample, an ensemble of 101 photochemical models is generated and run for theyear 2001 over Europe. The models' performance is quickly reviewed, and theensemble structure is analyzed. We found a strong diversity in the results ofthe models and a wide spread of the ensemble. It is noteworthy that manymodels turn out to be the best model in some regions and some dates.