Assimilation of GOME total‐ozone satellite observations in a three‐dimensional tracer‐transport model

TLDR

The model is a tracer‑transport model with parametrized stratospheric gas‑phase and heterogeneous ozone chemistry. The paper describes a data‑assimilation scheme for GOME total‑ozone data and focuses on its assimilation aspects and analysis results. TM3DAM, operational since early 2000, uses ECMWF meteorological fields to assimilate near‑real‑time GOME level‑2 ozone data via Kalman‑filter equations, producing daily analyses, five‑day forecasts, detailed error maps, and two‑year forecast‑minus‑observation statistics. The scheme is computationally efficient and its analyses agree well with TOMS and Brewer observations. © 2003 Royal Meteorological Society.

Abstract

Abstract A data‐assimilation scheme to assimilate the Global Ozone Monitoring Experiment (GOME) total‐ozone data is described. The corresponding software (called TM3DAM) has been operational since early 2000 and is used to produce daily ozone analyses and five‐day ozone forecasts. The model is a tracer‐transport model with a parametrized description of stratospheric gas‐phase and heterogeneous ozone chemistry. It is driven by operational meteorological fields from the ECMWF numerical weather‐prediction model. TM3DAM analyses near‐real‐time level‐2 ozone data from the GOME instrument on the ESA ERS‐2 satellite. The focus of this paper is on the data‐assimilation aspects and the analysis results. The assimilation approach is based on the Kalman‐filter equations and provides detailed and realistic maps of the forecast error. The analysis scheme is nevertheless computationally efficient. The forecast‐minus‐observation statistics, accumulated over a two‐year period, are described in detail. A comparison with TOMS and Brewer observations shows good agreement. Copyright © 2003 Royal Meteorological Society