Evaluation of algorithms developed for adaptive grid air quality modeling using surface elevation data

摘要: Abstract An adaptive grid model is being developed to reduce the resolution-related uncertainty in air quality predictions. By clustering nodes regions where errors pollutant concentrations would potentially be large, expected generate much more accurate results than its fixed, uniform counterparts. The repositioning of performed automatically using a weight function that assumes large values when curvature (change slope) fields large. Despite movement nodes, structure does not change: each node retains connectivity same neighboring nodes. Since there no priori knowledge movement, input data must re-gridded after adaptation step, throughout simulation. Emissions are one major inputs and mapping them adapted computationally intensive task. Efficient intersection algorithms take advantage unchanging structure. Here, evaluated surface elevation data. Two sets reduced one-fourth their sizes as well grids. first set contains important terrain features near boundaries while second has all far away from boundaries. compression maximum error 25% smaller compared with number associated 60% compression. These show algorithm potential significantly improving accuracy predictions, especially changing slope Indeed, preliminary application, displayed superior performance capturing details plumes emission sources. efficient overhead involved intersecting cells sources limiting simulations.