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Conference Agenda

Overview and details of the sessions and sub-session of this conference. Please select a date or session to show only sub-sessions at that day or location. Please select a single sub-session for detailed view (with abstracts and downloads if available).

Session Overview
A2: ID.10663 AMFIC
Tuesday, 05/Jul/2016:
11:30am - 12:30pm

Session Chair: Ronald van der A
Session Chair: Pucai Wang
Workshop: Atmosphere & Climate
Location: Lecture Hall, 2nd Floor, LIESMARS, Wuhan University

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Oral presentation

Monitoring And Assessment Of Regional Air Quality In China Using Space Observations (MarcoPolo)

Ronald van der A1,2, Jieying Ding1, Bas Mijling1, Jianhui Bai3, Pinhua Xie4, Jenny Stavrakou5, Kostas Kourtidis6, MariLiza Koukouli7, Gerrit de Leeuw8, Vassilis Amiridis9, Yong Xue10

1KNMI, The Netherlands; 2NUIST, China; 3IAP, China; 4AIOFM, China; 5BIRA-IASB, Belgium; 6DUTH, Greece; 7AUTH, Greece; 8FMI, Finland; 9NOA, Greece; 10LMU, UK;

The main objective of the MarcoPolo project is to improve air quality monitoring, modelling and forecasting over China using satellite data and by combining Chinese and European expertise. Due to the strong economic growth in the China, emissions of air pollutants like NO2, SO2, aerosols, and CO are rapidly growing. On the other hand, implementation of cleaner technology is reducing the emissions. In the MarcoPolo project we study the effect of economic growth and air quality regulations by monitoring the main air pollutants from space. The time series and trends in SO2 and NO2 derived from satellite observations for the last 10 years will be discussed. Satellite observations are also used to derive emission estimates for NOx, SO2, PM and biogenic sources. By combining these emission data with known information from the ground, a new emission database for MarcoPolo will be constructed. The improved emission inventory will be input to the regional and local air quality models.

van der A-Monitoring And Assessment Of Regional Air Quality_Cn_version.pdf
van der A-Monitoring And Assessment Of Regional Air Quality_ppt_present.pdf

Oral presentation

The Study Of Biogenic Organic Compound Emissions And Ozone In A Subtropical Bamboo Forest

Jianhui Bai1, Alex Guenther2,3, Andrew Turnipseed2, Duhl Tiffany2, Xiaowei Wan1, Yimei Wu1, Ronald van der A4, Shuquan Yu5, Bin Wang5

1LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, China, People's Republic of; 2National Center for Atmospheric Research, Boulder, CO 80307, USA; 3Now at Pacific Northwest National Laboratory, Richland WA 99352, USA and Washington State University, Pullman WA 99164, USA; 4KNMI, Utrechtseweg 297, 3731GA De Bilt, The Netherlands; 5School of Forestry & Bio-technology Zhejiang Agriculture and Forestry University,Hangzhou 311300.;

Abstract: Biogenic volatile organic compound (BVOC) emissions were measured using the relaxed eddy accumulation technique in a subtropical bamboo forest, China from July, 2012 to January, 2013, their variation characteristics were obtained. Based on PAR energy balance, empirical models of BVOC emissions were developed, which can capture most variations of BVOC emissions. Ozone empirical model was also developed and the simulated ozone concentrations were in agreement with the observations. The sensitivity test shows that ozone is increased with the increase of isoprene emission in most conditions.

Key words: Biogenic volatile organic compounds, subtropical bamboo forest, empirical model, ozone

Bai-The Study Of Biogenic Organic Compound Emissions And Ozone_Cn_version.pdf


Variability in tropospheric ozone over China derived from assimilated GOME-2 ozone profiles

Jacob Van Peet, Ronald Van Der A

KNMI, The Netherlands;

A tropospheric ozone dataset is derived from assimilated GOME-2 ozone profiles for the period 2007-2012. GOME-2 onboard Metop-A is a nadir looking, cross-track scanning spectrometer with global coverage. Ozone profiles are retrieved with the OPERA algorithm, using the optimal estimation retrieval technique. The retrievals are done on a spatial resolution of 160×160 km on 16 layers ranging from the surface up to 0.01 hPa.

The GOME-2 observations are assimilated into the chemical transport model TM5 using a sequential Kalman filter algorithm. TM5 is configured to run on a 3×2 (lon × lat) degree grid on 44 layers from the surface to the top of the atmosphere. The sensitivity and vertical resolution of the retrievals are taken into account by using the averaging kernels in the data assimilation. In this way, the data assimilation algorithm maintains the high resolution vertical structures of the model, while being constrained by observations with a much lower vertical resolution. The TM5 model covariance matrix is too large to store completely, therefore it is parameterized into a time dependent standard deviation field and a constant correlation field. To prevent contamination of the anthropogenic tropospheric signal with stratosphere-troposphere intrusions, we use the lowest six kilometers of the TM5 model as tropospheric ozone column.

We will show new results from an updated and extended dataset, with a focus on the impact of the assimilation on the ozone concentration over the Himalayas in the model and the spatial patterns and temporal variability of tropospheric ozone over China.

Van Peet-Variability in tropospheric ozone over China derived_Cn_version.pdf

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