Incorporating TOMS Ozone Measurements into the Prediction of the Washington, D.C., Winter Storm during 24–25 January 2000

Abstract

In this study, a methodology is proposed for incorporating total column ozone data from the Total Ozone Mapping Spectrometer (TOMS) into the initial conditions of a mesoscale prediction model. Based on the strong correlation between vertical mean potential vorticity (MPV) and TOMS ozone (O3) that was found in middle latitudes at both 30- and 90-km resolutions, using either analyses or 24-h model forecasts, a statistical correlation model between O3 and MPV is employed for assimilating TOMS ozone in a four-dimensional variational data assimilation (4DVAR) procedure. A linear relationship between O3 and MPV is first assumed: O3 = α(MPV) + ?. The constants α and ? are then found by a regression method. The proposed approach of using this simple linear regression model for ozone assimilation is applied to the prediction of the 24?25 January 2000 East Coast winter storm. Three 4DVAR experiments are carried out assimilating TOMS ozone, radiosonde, or both types of observations. It is found that adjustments in model initial conditions assimilating only TOMS ozone data are confined to the upper levels and produce almost no impact on the prediction of the storm development. However, when TOMS ozone data are used together with radiosonde observations, a more rapid deepening of sea level pressure of the simulated storm is observed than with only radiosonde observations. The predicted track of the winter storm is also altered, moving closer to the coast. Using NCEP multisensor hourly rainfall data as verification, the 36-h forecasts with both TOMS ozone and radiosonde observations outperform the radiosonde-only experiments. These results indicate that TOMS ozone data contain valuable meteorological information, which can be used to improve cyclone prediction.