Generating high spatial resolution analyses of SBUV stratospheric ozone for calculating the tropospheric ozone residual (TOR)

Abstract

In the last decade, daily analyses of the Tropospheric Ozone Residual (TOR), which is an estimate of the vertically- integrated ozone in the troposphere, has been calculated as the difference between the vertically-integrated stratospheric ozone using data from the Solar Backscatter Ultraviolet (SBUV) remote sensing system and the total ozone from the Total Ozone Mapping Spectrometer (TOMS). Comparison of daily values of the TOMS/SBUV TOR with daily values of the surface ozone concentration and of the vertically-integrated ozone in the troposphere using ozonesonde data provided poor correlations. Reasonably good correlations were noted for longer-term (monthly, seasonally, and annually) averaged data. One of the major problems in applying SBUV data with TOMS data to develop daily estimates of the TOR is the difference in the spatial resolution. The SBUV instrument is a non-scanning, downward- looking radiometer. Data are only collected with 200-km spatial resolution along the orbital track of the satellite on which the instrument resides. The orbital tracks are as much as 25˚ longitude apart. The TOMS total ozone data, on the other hand, are collected globally on a daily basis at 50 km spatial resolution. The SBUV data gaps have been traditionally filled using conventional interpolation procedures so that the stratospheric ozone from the SBUV instrument would be available at the data locations of the TOMS instrument. Conventional interpolation procedures that have been used to fill the SBUV data gaps (e.g., linear and higher order spatial regression, kriging, basis functions, neural networks) have lacked the scientific methodology to include rigorously essential sources of physical knowledge and the conceptual organization to account for composite space-time variability effects; and, therefore, lack the ability to account for features that may exist between SBUV data sampling tracks. This factor is a cause of major errors found in the daily values of the TOMS/SBUV TOR. The objective of this study is to find an interpolation procedure that will provide significantly improved analyses of SBUV stratospheric ozone in the regions defined by the SBUV data gaps than is presently be acquired using conventional interpolations procedures. For this study, the Bayesian Maximum Entropy (BME) interpolation procedure of Modern Spatiotemporal Geostatistics was used to integrate efficiently salient physical knowledge about ozone in order to generate realistic analyses of ozone distribution across space and time. In addition to the satellite ozone measurements, BME interpolation procedure used secondary (soft) information such as the total ozone-tropopause pressure empirical relationship. The results suggested that BME interpolation procedure could eliminate a major source of error in the TOMS/SBUV TOR analyses (i.e., interpolation error), producing high spatial resolution analyses that are more accurate and informative than those presently produced using conventional interpolation techniques.