ENVISAT Atmospheric Data Sets - Quality Monitoring and Consolidation
Brizzi, Gabriele1; Saavedra de Miguel, Lidia1; De Laurentis, Marta1; Iannone, Rosario Quirino1; Scarpino, Gabriella1; Casadio, Stefano1; Casadio, Stefano1; Dehn, Angelika2; Fehr, Thorsten2
1Serco, ITALY; 2ESA/ESRIN, ITALY
The three atmospheric chemistry sensors on-board of ENVISAT (GOMOS, MIPAS, and SCIAMACHY) have been successfully operated by ESA over a period of more than 10 years recording an invaluable amount of measurements relevant for the Atmospheric-Chemistry community. During ENVISAT operational phase (from March 2002 to April 2012), instrument performances and data production were accurately monitored by a products quality assessment service, carried out since 2005 by the IDEAS team (Instrument Data quality Evaluation and Analysis Service).
Data quality monitoring is therefore a fundamental aspect of EO mission as the quality of satellite measurements is variable and has to be managed to a well-established level of confidence before data provision to users.
The main purpose of the service was to detect as early as possible any possible anomaly and mitigate the impact on the quality of the data products. Following the anomaly detection, the products were further checked with the instrument specific tool. The outputs were represented by daily reports or cyclic reports, and anomaly reports.
QC monitoring is not only limited to the operational phase of a mission. After the sudden end of the ENVISAT mission, harmonization and consolidation of the acquired data sets have become key point for the ESA Data Quality and Algorithms Management Office (EOP-GMQ). For this reason, Instrument Processor Facility are continuously improved and dedicated data re-processing campaigns are performed in order to meet scientific requirements and deliver high quality data products to the scientific community. In this paper the performance of the three atmospheric instruments, the status of their data sets, the improvements introduced by the latest processors, and comparisons with previous dataset will be presented, together with an overview of foreseen evolutions.