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Data assimilation: adding value to Envisat chemistry observations

William Lahoz(1)

(1) NILU, Instituttveien 18, Kjeller 2027, Norway


The routine confrontation of observations with models in the data assimilation method has been fundamental to Numerical Weather Prediction (NWP), adding value to both observations and models, and significantly improving forecast skill. Building on this experience, in a pioneering activity for a research satellite mission, data assimilation has been used to add value to Envisat chemistry observations from the GOMOS, MIPAS and SCIAMACHY instruments. To illustrate Envisat chemical data assimilation activities, we select examples from the FP5 ASSET project. We show data assimilation has added value to both the Envisat data and the models used to assimilate Envisat data. We draw lessons from the assimilation of Envisat data, with reference to adding value to current research satellite missions, and preparing for future research satellite missions.


Workshop presentation