Near-real-time approach to assimilation of satellite-retrieved 3D ozone fields in a global model using simplified Kalman filter

In this paper, a near-real-time approach to data assimilation for the estimation of the three-dimensional global ozone distribution based on the transport model TM3 is presented. TM3 uses the meteorological data from European Centre for Medium-Range Weather Forecasts to transport the ozone field. The ozone profiles used in the assimilation are retrieved from measurements of the Global Ozone Monitoring Experiment instrument (GOME) on the European Space Agency's European Remote Sensing-2 satellite. A simplified Kalman filter technique is chosen as a data assimilation technique since it not only estimates the ozone distribution globally but also quantifies the uncertainty in its estimate. The NRT data assimilation technique is demonstrated for the period of March-June 2000. The results presented in this paper show that the estimated profiles deviate from the measurements by, on a global average, 15% of the ozone values in pressure layer 163-119 hPa and ranging from 6% to 2% of the ozone values between 119 hPa and 40 hPa. The deviation of the estimate from the measurements decreases with altitude and reaches 0.05% of the ozone value in the layer 2-0 hPa. Comparison with ozone-sonde observations shows that the model driven by observed meteorological fields is capable of describing the main spatial and temporal variations. Comparison with data from the Halogen Occultation Experiment instrument on the Upper Atmosphere Research Satellite shows that assimilating the retrieved profiles from GOME improves the vertical description of ozone profiles mainly in the stratosphere and the upper troposphere. Moreover, the integrated ozone-profile estimate is compared with the total-column ozone measurements made by the Total Ozone Mapping Spectrometer. This shows that assimilating the ozone profiles results in a better description of the total column than the ten-day forecast.