HR: 08:00h
AN: A31A-01    [PDF]
TI: Imaging Ozone Distributions Across Space-Time Using Satellite Data And Physical Information
AU: * Serre, M L
EM: marc_serre@unc.edu
AF: Center for the Advanced Study of the Environment (CASE), Department of Environmental Science and Engineering, University of North Carolina-Chapel Hill, 104 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599-7431 United States
AU: Christakos, G
EM: george_christakos@unc.edu
AF: Center for the Advanced Study of the Environment (CASE), Department of Environmental Science and Engineering, University of North Carolina-Chapel Hill, 104 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599-7431 United States
AU: Kolovos, A
EM: kolovos@email.unc.edu
AF: Center for the Advanced Study of the Environment (CASE), Department of Environmental Science and Engineering, University of North Carolina-Chapel Hill, 104 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599-7431 United States
AU: Vukovich, F
EM: fvukovich@raleigh.saic.com
AF: Science Applications International Corp. (SAIC), 615 Oberlin Road, Suite 300, Raleigh, NC 27605 United States
AB: Atmospheric studies often require the generation of high resolution maps of ozone distribution across space and time. The high natural variability of ozone concentrations and the different levels of accuracy of the algorithms used to generate data from remote sensing instruments introduce major sources of uncertainty in ozone modelling and mapping. These elements of atmospheric ozone distribution cannot be confronted satisfactorily by means of conventional interpolation and statistical data analysis. We suggest that the techniques of Modern Spatiotemporal Geostatistics (MSG) can be used efficiently to integrate salient (although of varying uncertainty) knowledge bases about atmospheric ozone in order to generate and update realistic pictures of ozone distribution across space-time. MSG techniques rely on a powerful scientific methodology that does not make the restrictive assumptions of previous techniques. A numerical study is discussed involving data sets generated by measuring instruments on board the Nimbus 7 satellite. In addition to exact (hard) ozone data, the MSG techniques process uncertain measurements and secondary (soft) information in terms of total ozone-tropopause pressure empirical relationships. The proposed total ozone analysis can take into consideration major sources of error in the TOMS/SBUV TOR (related to data sampling etc.) and produce high spatial resolution maps that are more accurate and informative than those obtained by conventional interpolation techniques.
DE: 0350 Pressure, density, and temperature
DE: 0365 Troposphere--composition and chemistry
DE: 0394 Instruments and techniques
DE: 0399 General or miscellaneous
SC: Atmospheric Sciences [A]
MN: 2003 Fall Meeting