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摘要
In current air quality models, distinct process operators are applied sequentially to pollutant concentration fields. A common time step is used to synchronize all the processes. Usually, the characteristic time for advection, which is equal to the grid length divided by the wind speed, is selected as the common step. Since the same time step is used everywhere in the domain, the maximum wind speed and minimum grid length determine the step size. This leads to computational inefficiency in cells where process characteristic times are much longer than the time step.
A variable time-step algorithm was developed that allows each grid cell to have its own time step. Concentrations in cells with shorter time steps are updated using fluxes from cells with longer time steps. Fluxes from cells with shorter time steps to cells with longer time steps are kept in reservoirs. Concentrations in cells with longer time steps remain constant until the time levels are synchronized. At the time of synchronization the mass in each reservoir is added to the corresponding cell.
A two-dimensional implementation of the algorithm that uses the same time step in each vertical column is described. PM2.5 estimates obtained by using variable time steps are, on average, within 3% of those obtained by using a single time step. Larger differences are observed for PM2.5 components, especially for sulfate, which is 12% higher in winter. The differences in light extinction are also within 3% and those in ozone are within 1%. The computation time decreased by 50% in a winter episode largely due to the economy realized in aerosol equilibrium calculations. The time saved by this algorithm can be spent in increasing the process detail in air quality models or improving their computational accuracy.
- 出版日期2010-10
