Air quality ensemble forecasting: an experimental ensemble design and a Kalman filter correction

Ensemble weather forecasts have been extensively evaluated over the past decade, and found to provide better accuracy than any single numerical model run. Different NWP models usually perform better for different situations, and often their behavior cannot be anticipated. Hence, their combination into a multi-model ensemble is usually fruitful. NWP ensembles have been created with different ICs and/or BCs, parameterizations within a single model, numerics within a single model, and models, trying to account different sources of uncertainties. The ensemble technique can potentially yield similar benefits to air-quality (AQ) modeling, because there are similar code complexity and constraints. Different AQ models can be better for different air-pollution episodes, also in ways that cannot always be anticipated. For AQ, the ensemble-mean can be created similarly with different inputs, parameterizations within a single model , numerics within a single model, and models. Given the nonlinear nature of photochemical reactions, the ensemble spread may rapidly account for the uncertainties associated with each component of the modeling process.

Results of an AQ ensemble forecast system will be presented. The system includes the Community Multiscale Air Quality Model (CMAQ), driven by MM5 and the Mesoscale Compressible Community Model (MC2). CMAQ is run with a resolution of 12 and 4 km. Furthermore, for each of the four mesoscle model/resolution combinations CMAQ is run three times with different settings, leading to 12 different ensemble members. Moreover, a Kalman filter is applied to each ensemble member, and the filter performance is tested. The spatial domain considered in the simulation includes the Lower Fraser Valley (LFV) of British Columbia.