Improvement of air quality forecasts with satellite and ground based particulate matter observations

被引：11
|
作者：
Hirtl, M. [1 ]
Mantovani, S. [2 ]
Krueger, B. C. [3 ]
Triebnig, G. [4 ]
Flandorfer, C. [1 ]
Bottoni, M. [2 ]
Cavicchi, M. [5 ]
机构：
[1] ZAMG Cent Inst Meteorol & Geodynam, Sect Environm Meteorol, Vienna, Austria
[2] SISTEMA GmbH, Vienna, Austria
[3] BOKU Univ Nat Resources & Life Sci, Inst Meteorol, Vienna, Austria
[4] EOX IT Serv GmbH, Vienna, Austria
[5] MEEO Srl, Ferrara, Italy
来源：
ATMOSPHERIC ENVIRONMENT | 2014年 / 84卷
关键词：
PM10; forecasts; Support Vector Regression; MODIS AOT; WRF/Chem; CHEMISTRY-TRANSPORT MODELS; AEROSOL; EUROPE; OZONE;
D O I：
10.1016/j.atmosenv.2013.11.027
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Daily regional scale forecasts of particulate air pollution are simulated for public information and warning. An increasing amount of air pollution measurements is available in real-time from ground stations as well as from satellite observations. In this paper, the Support Vector Regression technique is applied to derive highly-resolved PM10 initial fields for air quality modeling from satellite measurements of the Aerosol Optical Thickness. Additionally, PM10-ground measurements are assimilated using optimum interpolation. The performance of both approaches is shown for a selected PM10 episode. (C) 2013 Elsevier Ltd. All rights reserved.
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页码：20 / 27
页数：8
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