Konstantinos Kourtidis Georgoulias Aristeidis Georgoulias Antonios Gasteratos Konstantinos Konstaninidis AMFIC Web Data Base AMFIC Final Meeting - Beijing 23 October 2009 Democritus University of Thrace Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric Pollutants

1. Current status: Data brought in common gridded format and inserted in the databaseData brought in common gridded format and inserted in the database AMFIC web data base is ready and functional,AMFIC web data base is ready and functional, Fully available products: SCIAMACHY WFM-DOAS v1.0 XCH 4 dry air column SCIAMACHY WFM-DOAS v0.6 CO column SCIAMACHY SO2 column SCIAMACHY NO2 tropospheric and total column

The example of Methane data:

FRONT BACK Methane product

The example of Methane data: BACK

Xian

Some advantages of AMFIC web data base: By breaking huge global files to many gridded ascii files we make the process of those data easier for users interested in specific spots.By breaking huge global files to many gridded ascii files we make the process of those data easier for users interested in specific spots. Format and accessibility enables easy validation/comparison of several products with GND-based, modelled and other data for selected regionsFormat and accessibility enables easy validation/comparison of several products with GND-based, modelled and other data for selected regions The users can request either plots or ascii files for a quick look in secondsThe users can request either plots or ascii files for a quick look in seconds Users interested in global data sets can just download the whole dataset using Wget or a web ripper softwareUsers interested in global data sets can just download the whole dataset using Wget or a web ripper software Suitable for educational purposesSuitable for educational purposes

A case study on the use of Methane data 3. Analysis * The XCH 4 SCIAMACHY data appearing on the 4 figures have been scaled by 1.02 as discussed in Schneising et al., 2009

A case study on the use of Methane data 3. Analysis 1.The good agreement between the satellite and ground observations is probably due to the high altitude of the station. 2. According to Xiong et al., 2009 the (August-September) AIRS peak is connected to methane transport to the upper troposphere from the monsoon-Tibetan anticyclone system. 3. According to Xiong et al., 2009 the decrease of AIRS vmr thereafter is connected to the withdrawal of the monsoon and the dissipation of the Tibetan anticyclone. 4.The good agreement between SCIAMACHY and AIRS observations could be indicative of the transport of methane to the upper troposphere.

A case study on the use of Methane data 3. Analysis The good agreement between SCIAMACHY and AIRS observations could be indicative of the transport of methane to the upper troposphere in the region around the Tibetan Plateau.

Discussion: No comments received so far from the partners providing data about the terms of use/copyright/ availability of full datasets to the users.No comments received so far from the partners providing data about the terms of use/copyright/ availability of full datasets to the users. We hence assume that partners agree with what is currently stated in the web data baseWe hence assume that partners agree with what is currently stated in the web data base HCHO data not available to us in a form dowloadable with reasonable effort, hence not included (each day in separate folders, hence not downloadable with DownThemAll or WGet scripts; currently not available in specified folders).HCHO data not available to us in a form dowloadable with reasonable effort, hence not included (each day in separate folders, hence not downloadable with DownThemAll or WGet scripts; currently not available in specified folders).

3. Automatic Plume Detection: Within the AMFIC data base demonstration of image analysis techniques for automatic plume detection Three-step procedure: 1.Reconstruct the missing parts of maps (images), which lack information due to the daily coverage stripes of several satellite instruments or due to cloudiness, albedo characteristics etc, with the use of Cellular Automata (CA). 2.Extract only the regions on the map which are covered by significant plumes with the use of a cut-off filter. 3.Calculation of the area covered from each plume. Matlab image analysis algorithm runs off-line, integration not feasible Matlab image analysis algorithm runs off-line, integration not feasible

Cellular Automaton approach: A cellular automaton requires: 1.a regular lattice of cells covering a portion of a four dims space 2.a set of variables C attached to each cell giving its local state at the time t=0, 1, 2, … 3.a rule R={R 1,R 2,…,R m } which specifies the time evolution of the states in the following way: where designate the cells belonging to a given neighbor- hood of cell where designate the cells belonging to a given neighbor- hood of cell In our case each cell is represented by an image pixel The rule R applied here is the extraction of the average from pixels in the neighborhood (Moore neighborhood ) that contain information

An example for GOME-2: GOME-2 on MetOp-A (October 2006) 4 channels cover the full spectral range from to µm 4 channels cover the full spectral range from to µm Resolution nm Resolution nm Pixel size 80x40 km 2 Pixel size 80x40 km 2 Scan width 1920 km Scan width 1920 km Global coverage within 3 day Global coverage within 3 dayInput NO2 daily maps TEMIS website Process 1 Reconstruct the missing parts Process 2 Define areas with significant plumes Process 3 Calculate the area each plume covers

INPUT: NO 2 daily maps from TEMIS web data base Time period 28/3/2008-6/4/ /329/330/3 31/3 1/42/43/4 4/4 5/46/4

PROCESS 1: Reconstruct full maps (images) Time period 28/3/2008-6/4/ /329/330/3 31/3 1/42/43/4 4/4 5/46/4

PROCESS 2:Define areas with significant plumes using color filters, time period 28/3/2008-6/4/2008 PROCESS 2: Define areas with significant plumes using color filters, time period 28/3/2008-6/4/ /329/330/3 31/3 1/42/43/4 4/4 5/46/4

PROCESS 3:Calculate the areas covered from significant plumes PROCESS 3: Calculate the areas covered from significant plumes Time period 28/3/2008-6/4/ /329/330/3 31/3 1/42/43/4 4/4 5/46/4

If we have larger stripes (e.g. SCIAMACHY daily maps) … Example for 28/3/2008 vsvs

3. Work status Objectives The objective of this WP is to exploit the outcome of the WP2, WP3, WP4 and WP5, to determine the applicability of satellite observations for studying and monitoring transport of air pollutants. It is to demonstrate how to use data from satellites to explore processes relevant to the transport of air pollution and to monitor these processes. It is also to integrate this ability with the GEOSS, INSPIRE and GMES requirements. Finally, to provide an extensive system assessment report, suitable for feeding GEOSS and GMES requirements, as well as an assessment of options and operational requirements for long-term operation of such a satellite system. For the achievement of these goals, data will be homogenized and made available in a web database, and a workshop will be organized where all potential end-users will be invited and will interact with the project partners.  Accomplished

3. Work status Description of work Task 6.1: The different data fields, observed, modelled and/or assimilated (NO2, ozone, SO2, HCHO, CO, CH4, aerosol) will be homogenised and brought into a common spatial and temporal format, complying with INSPIRE requirements. At the project website, a web database will host these data. Underlying the database structure, a set of tools will allow easy manipulation and analysis of the data, more specifically: Automatic detection of features such as plumes and monitoring of their evolution, Cluster analysis Data co-variance analysis, enabling the detection of emission signatures of different sources Export of data in different formats  Accomplished

Aristeidis K. Georgoulias Konstantinos Kourtidis Antonios Gasteratos Konstantinos Konstaninidis AMFIC Web Data Base AMFIC Final Meeting - Beijing 23 October 2009 Democritus University of Thrace Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric Pollutants

THERMOPOLIS2009 ESA campaign, Athens, Greece, summer 2009  Study of urban heat island  Use of satellite data  Thermal Stress Indices  Forecasting

THERMOPOLIS2009 Ground Stations.

ASTER imagery acquired during over Athens on July 16th (09:22 UTC), during the THERMOPOLIS campaign. This is a false colour composite image produced from the raw visible and near infrared channels of the sensor (R3G2B1). The sensor records information in 15 spectral channels whereas its spatial resolution varies from 15 m (visible to near infrared channels) to 30 m (shortwave infrared channels) and 90 m (thermal infrared channels). National Observatory of Athens.

LANDSAT TM imagery acquired on July 24th (08:53 UTC) over the Athens during the THERMOPOLIS campaign. The sensor records information in 7 spectral bands and it has a spatial resolution 30 m in the reflective bands (visible to shortwave infrared) and 120 m in the thermal infrared band. National Observatory of Athens.

Land Surface Temperature (LST) map for July 18th, one of the days of the THERMOPOLIS expedition, computed from the METEOSAT-2 SEVIRI radiometer. National Observatory of Athens.

Map with daily TERRA+AQUA (FLASHFLUX v2F) Shortwave TOA Flux (W/m2) (a), and Longwave TOA Flux (W/m2) (b) data for 17/7/2009. DUTH.

CALIPSO Lidar browse images from expedited release dataset (v2.02). Attenuated backscatter coefficient at 532nm profile google earth picture for 17/7/2009. Overpass time ~11:45UTC. DUTH.

Maps with Level 3 MODIS (C005) AOD550 data (a) and Ångström exponent (b) data for 17/7/2009. DUTH.

DUTH Sonic anemometer on 9-m tower for sensible/latent heat fluxes measurement.

Selected spectra on 17 of July 2009 at 08:00, 09:00, 10:00 and 12:00 LT, measured with the PGS-100 spectrophotometer. DUTH/NOA.

Athens transect, 21 July UVEG.

UVEG

Thermography. UVEG.

Land surface temperature map (left panel) and urban heat island map (right panel) for Athens heat wave.

AMFIC continuation  LST maps for Chinese cities  LST + AirT  Thermal Stress Indices  Maps of Cooling Degree Days for energy consumption  TOA SW/LW  TOA SW/LW + Aerosols for radiative forcing of aerosol pollution