Ensemble Air Quality Forecasting: Effects of Perturbations in Meteorology and Emissions

Hogrefe, Christian; Doraiswamy, Prakash; Colle, Brian A.; Demerjian, Kenneth L.; Hao, Winston; Beauharnois, Mark; Erickson, Michael J.; Souders, Matthew B.; Ku, Jia‐Yeong; Sistla, Gopal

doi:10.1007/978-94-007-1359-8_70

Cited by 1 publication

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“…It is clear that the aerosol optical property estimation between the models is not consistent. Part of the poor model agreement likely stems from the fact that high resolution models, such as the WRF/CMAQ (Weather Research Forecast/Community Multi scale Air Quality) model strongly couples meteorological factors, PBL height and surface boundary conditions including emission inventories to estimate particulate concentrations and vertical distributions [15,16]. In addition, the humidification of the aerosols is very difficult to treat rigorously.…”

Section: Introductionmentioning

confidence: 99%

Assessing Surface PM2.5 Estimates Using Data Fusion of Active and Passive Remote Sensing Methods

Cordero

Malakar

et al. 2013

BJECC

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In this paper, we focus on estimations of fine particulate matter by combining MODIS satellite Aerosol Optical Depth (AOD) with Weather Research Forecast (WRF) PBL information using a neural network approach and assess its performance. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as relevant factors in estimating PM2.5 in passive radiometer and active lidar data at CCNY and demonstrate that the PBL height is the most critical additional parameter for accurate PM2.5. Furthermore, active measurements from both ground and satellite based lidar are used to show that summer WRF model PBL heights are most accurate. We then expand our analysis to a regional domain where daily estimations are obtained and compared with operational GEOS-CHEM PM2.5 product. Using our approach, we also create regional daily PM2.5 maps and compare against GEOS-CHEM outputs. Finally, we also consider additional improvements, where multiple satellite observations are used as regressors to predict PM2.5. These results illustrate the significant improvement we obtain within this framework in comparison to a "one size fits all continental scale approach".

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Section: Introductionmentioning

confidence: 99%