Presentation is loading. Please wait.

Presentation is loading. Please wait.

Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing 16-17 October 2008 Democritus.

Published byClement Gregory Modified over 9 years ago

Similar presentations

Presentation on theme: "Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing 16-17 October 2008 Democritus."— Presentation transcript:

1 Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing 16-17 October 2008 Democritus University of Thrace Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric Pollutants

2 1. Current status: AMFIC web data base is ready!AMFIC web data base is ready!

3 1. Current status: AMFIC web data base is ready!AMFIC web data base is ready! The already fully analyzed data are being uploadedThe already fully analyzed data are being uploaded The example of Methane data: The first fully inserted product in our data base is SCIAMACHY WFM-DOAS v1.0 XCH 4 dry air columnar dataset* * SCIAMACHY WFM-DOAS v0.6 CO columnar data are ready and will be uploaded within the next 15 days * SCIAMACHY SO 2 columnar data are being processed and will be uploaded within the next month

4 The example of Methane data: CO & SO 2 coming soon!

5 The example of Methane data: FRONT BACK Methane product

6 The example of Methane data: BACK

7

8

9

10

11 Xian

12

13

14 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 spotsBy breaking huge global files to many gridded ascii files we make the process of those data easier for users interested in specific spots This analysis enables easy validation of several products for selected regions and comparison with model resultsThis analysis enables easy validation of several products for selected regions and comparison with model results The users can request either plots or ascii files even for a quick look in a few secondsThe users can request either plots or ascii files even for a quick look in a few 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 The data base will be perfect even for educational purposesThe data base will be perfect even for educational purposes

15 2. AIRSAT web data base integration: Satellite Earth simulator-AIRSAT http://www.satellite-earth-simulator.com/ Satellite Data Sources: TOMS (Earth Probe TOMS)TOMS (Earth Probe TOMS) SCIAMACHY (ENVISAT)SCIAMACHY (ENVISAT) GOME (ERS-2)GOME (ERS-2) MODIS (AQUA)MODIS (AQUA) Meteorological Data Source: NCEP/NCAR Reanalysis ProjectNCEP/NCAR Reanalysis Project

16 2. AIRSAT web data base integration: Meteorological parameters Temperature Temperature Humidity Humidity Pressure Pressure Wind speed Wind speed Precipitation Precipitation Atmospheric data Ozone vertical column Ozone vertical column NO 2 vertical column NO 2 vertical column Aerosol Optical Thickness over ocean Aerosol Optical Thickness over ocean Cloud Optical Thickness Cloud Optical Thickness Cloud Top Temperature Cloud Top Temperature Optical Depth Land and Ocean Optical Depth Land and Ocean Atmospheric Water Vapour Atmospheric Water Vapour Ocean data Chlorophyll Chlorophyll Sea surface temperature, day/night Sea surface temperature, day/night Daily assimilated and gridded data sets could be included in this part of the data base (e.g. TEMIS 0.25x0.25 SO 2 gridded products)

17 An Example from AIRSAT: Data from WDC-RSAT (World Data Center for Remote Sensing of the Atmosphere http://wdc.dlr.de) geographical area data set time period

18 daily data in ascii format daily maps start animation select coordinates

19 animation

20 3. Automatic Plume Detection: Within AMFIC data base we initiate the implementation of image analysis techniques for the automatic plume detection Three steps 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.

21 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

22 An example for GOME-2: GOME-2 aboard MetOp-A (October 2006) 4 channels cover the full spectral range from 0.240 to 0.790 µm 4 channels cover the full spectral range from 0.240 to 0.790 µm Resolution 0.2-0.4 nm Resolution 0.2-0.4 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

23 NO 2 daily maps from TEMIS web data base www.temis.nl www.temis.nl Time period 28/3/2008-6/4/2008 28/329/330/3 31/3 1/42/43/4 4/4 5/46/4

24 An example for GOME-2: GOME-2 aboard MetOp-A (October 2006) 4 channels cover the full spectral range from 0.240 to 0.790 µm 4 channels cover the full spectral range from 0.240 to 0.790 µm Resolution 0.2-0.4 nm Resolution 0.2-0.4 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

25 Reconstructed maps (images) Time period 28/3/2008-6/4/2008 28/329/330/3 31/3 1/42/43/4 4/4 5/46/4

26 An example for GOME-2: GOME-2 aboard MetOp-A (October 2006) 4 channels cover the full spectral range from 0.240 to 0.790 µm 4 channels cover the full spectral range from 0.240 to 0.790 µm Resolution 0.2-0.4 nm Resolution 0.2-0.4 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

27 Define areas with significant plumes using color filters Time period 28/3/2008-6/4/2008 28/329/330/3 31/3 1/42/43/4 4/4 5/46/4

28 An example for GOME-2: GOME-2 aboard MetOp-A (October 2006) 4 channels cover the full spectral range from 0.240 to 0.790 µm 4 channels cover the full spectral range from 0.240 to 0.790 µm Resolution 0.2-0.4 nm Resolution 0.2-0.4 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

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

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

31 3. Work to be done… Data to be analyzed and uploaded to the data base within the next months: SCIAMACHY & OMI Tropospheric NO 2 dataSCIAMACHY & OMI Tropospheric NO 2 data 2003-end of project 2003-end of project SCIAMACHY Tropospheric HCHO dataSCIAMACHY Tropospheric HCHO data 2003-end of project 2003-end of project For the time being the image analysis algorithm runs off-line For the time being the image analysis algorithm runs off-line Either it will be integrated in AMFIC web data base so as to run on-line or the matlab codes will be available to the users via links The integration of AIRSAT is being scheduled The integration of AIRSAT is being scheduled

32 Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing 16-17 October 2008 Democritus University of Thrace Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric Pollutants

Download ppt "Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing 16-17 October 2008 Democritus."

Similar presentations

Martin G. Schultz, MPI Meteorology, Hamburg GEMS proposal preparation meeting, Reading, 15-16 Dec 2003 GEMS RG Global reactive gases monitoring and forecast.

Martin G. Schultz, MPI Meteorology, Hamburg GEMS proposal preparation meeting, Reading, Dec 2003 GEMS RG Global reactive gases monitoring and forecast.
WP 5 : Clouds & Aerosols L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute (KNMI) SCIAvisie Meeting, KNMI, De Bilt, 08-02-2013 Absorbing.

WP 5 : Clouds & Aerosols L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute (KNMI) SCIAvisie Meeting, KNMI, De Bilt, Absorbing.
 nm)  nm) PurposeSpatial Resolution (km) 317.51Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.

 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.
GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.

GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.
A U R A Satellite Mission T E S

A U R A Satellite Mission T E S
Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington.

Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington.
TEMIS user workshop, Frascati, 8-9 October 2007 Status of Central European NO 2 products Dominik Brunner, Daniel Schaub, Brigitte Buchmann Empa, Swiss.

TEMIS user workshop, Frascati, 8-9 October 2007 Status of Central European NO 2 products Dominik Brunner, Daniel Schaub, Brigitte Buchmann Empa, Swiss.
VENUS (Vegetation and Environment New µ-Spacecraft) A demonstration space mission dedicated to land surface environment (Vegetation and Environment New.

VENUS (Vegetation and Environment New µ-Spacecraft) A demonstration space mission dedicated to land surface environment (Vegetation and Environment New.
Remote Sensing of Pollution in China Dan Yu December 10 th 2009.

Remote Sensing of Pollution in China Dan Yu December 10 th 2009.
Polar Atmospheric Composition: Some Measurements and Products A Report on Action item STG3-A11 Jeff Key NOAA/NESDIS.

Polar Atmospheric Composition: Some Measurements and Products A Report on Action item STG3-A11 Jeff Key NOAA/NESDIS.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.

Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.

Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.

Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.
Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.

Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.
Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures - preliminary results using TRMM observations Chuntao Liu Department.

Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures - preliminary results using TRMM observations Chuntao Liu Department.
Aristeidis K. Georgoulias Konstantinos A. Kourtidis AMFIC Web Data Base II AMFIC Progress Meeting - Barcelona 24 June 2009 DRAGON II Symposium, Barcelona.

Aristeidis K. Georgoulias Konstantinos A. Kourtidis AMFIC Web Data Base II AMFIC Progress Meeting - Barcelona 24 June 2009 DRAGON II Symposium, Barcelona.
EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.

EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.
Quality of the official SCIAMACHY Absorbing Aerosol Index (AAI) level-2 product L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute.

Quality of the official SCIAMACHY Absorbing Aerosol Index (AAI) level-2 product L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
M. Van Roozendael, AMFIC Final Meeting, 23 Oct 2009, Beijing, China1 MAXDOAS measurements in Beijing M. Van Roozendael 1, K. Clémer 1, C. Fayt 1, C. Hermans.

M. Van Roozendael, AMFIC Final Meeting, 23 Oct 2009, Beijing, China1 MAXDOAS measurements in Beijing M. Van Roozendael 1, K. Clémer 1, C. Fayt 1, C. Hermans.

Similar presentations

Ads by Google