...
首页> 外文期刊>Instrumentation and Measurement, IEEE Transactions on >Remote Sensing Instruments Used for Measurement and Model Validation of Optical Parameters of Atmospheric Aerosols
【24h】

Remote Sensing Instruments Used for Measurement and Model Validation of Optical Parameters of Atmospheric Aerosols

机译:用于大气气溶胶光学参数测量和模型验证的遥感仪器

获取原文
获取原文并翻译 | 示例
           

摘要

With the dramatical climate changing that we are facing today, atmospheric monitoring is of major importance. Several atmospheric monitoring instruments are already being deployed for real-time surface-level retrieval of atmospheric composition, optical coefficients, particulate matter less than 2.5 μm in diameter (PM2.5), aerosol optical depth (AOD), and particle size distribution. However, these measurements are, in general, very cost intensive and can realistically be deployed only over very limited areas. Therefore, it is very important that advanced modeling methods be employed to fill these gaps and provide air quality predictions that can be used for forecasts as well as a better understanding of the interplay of meteorology, atmospheric emissions, and chemistry. In particular, for the New York State area, the New York State Department of Environmental Conservation uses Community Multiscale Air Quality (CMAQ) model to couple meteorology to local emissions, and there is intense interest in trying to assess the model performance beyond current surface network measurements. In particular, a deeper understanding of boundary layer processes can be made by experimentally exploring the vertical distribution model forecasts to better understand the underlying causes when model forecast results are not accurate. To this end, we develop a comprehensive Mie-scattering-based procedure including the effects of relative humidity that allows us to convert CMAQ aerosol distribution data into vertical-profile multiwavelength optical parameters that can be compared to column-integrated and vertical-profiling measurements to assess model performance and point to areas where the model is deficient. In particular, we make use of multiwavelength light detection and ranging (LIDAR), sunphotometer, ceilometer, and existing tapered element oscillating microbalance (TEOM) measurements to assess various vertical and column-integrated parameters of the CMAQ model und- r different stability conditions. In particular, we find that, for cases where the planetary boundary layer (PBL) is stable, the column-integrated AOD comparisons are in good agreement unlike the days with dynamic PBL. This is also consistent with observations that the TEOM PM2.5 trends are closely followed by the CMAQ model during these stable conditions. On the other hand, significant errors between the surface CMAQ PM2.5 and TEOM measurements can occur which can be traced to unphysically high particulate concentration profiles distributed too close to the surface not seen in ceilometer/LIDAR profiles. Finally, we note that, even for the stable cases, the multiwavelength optical depth data show that, for sufficiently low wavelengths, the column AOD in the model is underestimated, illustrating that there is a general underestimation of ultrafine (Aitken) particulates which can dramatically affect health.
机译:随着我们今天面临的剧烈气候变化,大气监测至关重要。已经部署了几种大气监测仪器来实时获取大气成分,光学系数,直径小于2.5μm的颗粒物(PM 2.5 ),气溶胶光学深度(AOD),和粒度分布。但是,这些测量通常会非常耗费成本,并且实际上只能在非常有限的区域内部署。因此,采用先进的建模方法来填补这些空白并提供可用于预测以及更好地了解气象,大气排放和化学相互作用的空气质量预测非常重要。特别是在纽约州地区,纽约州环境保护部使用社区多尺度空气质量(CMAQ)模型将气象学与当地排放量耦合,并且人们对尝试评估当前地面网络以外的模型性能表现出极大的兴趣。测量。特别是,可以通过实验探索垂直分布模型预测来更深入地了解边界层过程,以更好地理解模型预测结果不准确时的根本原因。为此,我们开发了一个基于米氏散射的综合程序,其中包括相对湿度的影响,使我们能够将CMAQ气溶胶分布数据转换为垂直剖面多波长光学参数,可以将其与色谱柱积分和垂直剖面测量结果进行比较评估模型性能,并指出模型不足的地方。特别是,我们利用多波长光检测和测距(LIDAR),日光光度计,云高仪和现有的锥形元素振荡微天平(TEOM)测量来评估CMAQ模型的各种垂直和列积分参数,而无需考虑不同的稳定性条件。特别是,我们发现,对于行星边界层(PBL)稳定的情况,与动态PBL时代相比,列积分AOD比较具有很好的一致性。这也与在这些稳定条件下CMAQ模型密切跟踪TEOM PM 2.5 趋势的观察结果一致。另一方面,在表面CMAQ PM 2.5 和TEOM测量之间可能会出现明显的误差,这可归因于分布异常高的颗粒浓度分布,这些分布太靠近在云高仪/ LIDAR分布图中看不到的表面。最后,我们注意到,即使对于稳定的情况,多波长光学深度数据也表明,对于足够低的波长,模型中的色谱柱AOD值被低估了,这表明人们普遍低估了超细(Aitken)微粒,这可能会大大降低影响健康。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号