J Air Waste Manag Assoc. 2014 Mar;64(3):291-308. doi: 10.1080/10962247.2013.872209.
Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/ hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges.
The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.
使用 CALPUFF 模型对颗粒物和水溶性污染物的扩散和湿沉降进行长程模拟,需要详细的逐时降水数据。在美国中北部等人口稀少的地区,地面降水测量站的间距可能过大,无法完整、准确地反映整个模拟区域内的逐时降水情况。卫星提供的遥感降水数据和美国国家气象局(National Weather Service)新一代雷达(NEXRAD)网在全国范围内的部署,为改善这些地区的数据缺乏提供了机会。在采用建模方案中的新降水估计方法之前,应将其与目前用于建模目的的地面降水测量值进行比较。本文比较了 2006 年至 2008 年间美国中北部 25 个地面站的逐时降水测量值与美国国家环境预报中心(National Center for Environmental Predictions,NCEP)作为第四阶段数据提供的、距每个选定降水站最近网格单元的雷达基降水测量值。结果表明,两种方法之间的统计一致性强烈依赖于地面每小时降水测量值的精度,精度为 0.1 英寸/小时或 0.01 英寸/小时。统计比较结果表明,在长程模拟中,在网格化扩散模型中使用网格化第四阶段降水数据,而不是依赖于在广泛分散的雨量计之间插值的降水数据,将更为准确。
目前,基于地面雨量计的降水事件和逐时数据被用于颗粒物和水溶性污染物的扩散和湿沉降建模,这导致数据覆盖范围存在潜在的不连续性,需要在监测站之间外推数据。美国大陆和附近地区都可获得基于雷达的降水数据,可解决这些数据空白,并为大型模拟区域内的逐时降水提供完整、准确的空间表示。