Daily peak ozone forecast in Istanbul

2010 ◽  
Vol 31 (2) ◽  
pp. 551-561 ◽  
Author(s):  
Y. S. Unal ◽  
S. Incecik ◽  
S. Topcu ◽  
A. Oztopal
Keyword(s):  
Author(s):  
E. Reimer ◽  
G. Wiegand ◽  
J. Flemming ◽  
M. Dlabka
Keyword(s):  

2014 ◽  
Vol 14 (16) ◽  
pp. 23201-23236 ◽  
Author(s):  
P. A. Cleary ◽  
N. Fuhrman ◽  
L. Schulz ◽  
J. Schafer ◽  
J. Fillingham ◽  
...  

Abstract. Air quality forecast models typically predict large ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline Differential Optical Absorption Spectroscopy (DOAS) observations in southeastern Wisconsin, and as predicted by the National Air Quality Forecast System. From 2008–2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008–2010 measurements of ambient ozone conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI and Muskegon, MI up to 6 times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha with little dependence on position of the ferry or temperature but with highest differences during evening and night. Concurrent ozone forecast images from National Weather System's National Air Quality Forecast System in the upper Midwestern region surrounding Lake Michigan were saved over the ferry ozone sampling period in 2009. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. The model 1 and 8 h ozone mean biases were both 12 ppb higher than observed ozone, and maximum daily 1 h ozone mean bias was 10 ppb, indicating substantial ozone over-prediction over water. Trends in the bias with respect to location and time of day or month were also explored showing non-uniformity in model bias. Extreme ozone events were predicted by the model but not observed by ferry measurements.


2011 ◽  
Vol 11 (24) ◽  
pp. 12901-12916 ◽  
Author(s):  
X. Tang ◽  
J. Zhu ◽  
Z. F. Wang ◽  
A. Gbaguidi

Abstract. In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O3 initial conditions, NOx initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.


2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. P. Moustris ◽  
P. T. Nastos ◽  
I. K. Larissi ◽  
A. G. Paliatsos

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.


2019 ◽  
Vol 124 (23) ◽  
pp. 13576-13592 ◽  
Author(s):  
Young‐Hee Ryu ◽  
Alma Hodzic ◽  
Gael Descombes ◽  
Ming Hu ◽  
Jérôme Barré

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 164 ◽  
Author(s):  
Yiping Wang ◽  
Hongyue Wang ◽  
Wuke Wang

Ozone pollution is currently a serious issue in China. As an important source of tropospheric ozone, the stratospheric ozone has received less concern. This study uses a combination of ground-based ozone measurements, the latest ERA5 reanalysis data as well as chemistry-climate model and Lagrangian Particle Dispersion Modeling (LPDM) simulations to investigate the potential impacts of stratospheric intrusion (SI) on surface ozone pollution episodes in eastern China. Station-based observations indicate that severe ozone pollution occurred from 27 April to 28 April 2018 in eastern China, with maximal values over 140 ppbv. ERA5 meteorological and ozone data suggest that a strong horizontal-trough exists at the same time, which leads to an evident SI event and brings ozone-rich air from the stratosphere to the troposphere. Using a stratospheric ozone tracer defined by NCAR’s Community Atmosphere Model with Chemistry (CAM-Chem), we conclude that this SI event contributed about 15 ppbv (15%) to the surface ozone pollution episode during 27–28 April in eastern China. The potential impacts of SI events on surface ozone variations should be therefore considered in ozone forecast and control.


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