MODELING SATELLITE OZONE DATA

Starting in the early 1970s, the decline in column ozone over much of the earth has received much attention. Satellite ozone data, with the advantage of global coverage, now play an important role in assessing long-term trends in ozone distributions. We consider a class of space- time regression models for the analysis of satellite data on a fixed l atitude, which take into account temporal and longitudinal dependence of the observations. The models can be used to test the uniformity of long-term trends in different longitudinal ozone series. Using the pro perty of circular matrices, explicit expressions of the likelihood fun ctions are obtained. Asymptotic properties of the parameter estimates are briefly discussed. A diagnostic method is proposed to tentatively select the orders in the noise terms of the models. The space-time reg ression models are applied to the total ozone mapping spectrometer (TO MS) data for trend assessment.