Assimilation of stratospheric ozone GOMOS data the isentropic transport model MIMOSA: Comparison between sub-optimal Kalman filter and kriging.
Charles Cot(1), Alain Hauchecorne(1), Julien Jumelet(1), David Cugnet(1) and Slimane Bekki(2)
(1) UVSQ, UPMC, CNRS, BP3, 91370 Verrières-le-Buisson, France
(2) UVSQ, UMPC, CNRS, BP3, 91370 Verrières-le-Buisson, France
GOMOS (Global Ozone Monitoring by Occultation of Stars) is the first space instrument dedicated to the study of the atmospheric composition by the technique of stellar occultations. The experiment aboard ENVISAT satellite was designed in order to evaluate stratospheric ozone concentration and trend (and other atmospheric minor constituents) over the Earth during the last few years.
Ozone concentration is variable in space and time. Spatial variability may be observed by a sufficient number of occultations and time variability by recording time series. GOMOS measurements are randomly distributed in space and time. A continuous field at grid points evenly spaced is needed to obtain a good estimate of ozone climatology and variability.
We present here a comparison between a heavy method consisting in the assimilation of GOMOS data in the high resolution isentropic transport model MIMOSA using a sub-optimal Kalman filter with a simple and fast multidimensional interpolation method: kriging.