Computer Science – Learning
Scientific paper
Aug 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006esasp.619...59k&link_type=abstract
Proceedings of the 3rd MSG RAO Workshop (ESA SP-619). 15 June 2006, Helsinki, Finland. Editor: D. Danessy, p.59
Computer Science
Learning
Scientific paper
Using operational SEVIRI Level 1.5 production, a set of 50 full disk ozone images taken once per week at 12 UT between February 2004 and January 2005 was produced. The horizontal resolution was reduced near 1 degree to fit the resolution of TOMS and assimilated Sciamachy products that are available on public websites. A first comparison revealed systematic errors as large as 50 D.U. dependent both on latitude and zenith distance of observation. The error could be interpreted as the result of a biased learning profile data set that was used to calculate the regression equations providing approximate background and foreground temperatures. Using a new climatology built with profiles of ozone, temperature and water vapour extracted from the analyses of ECMWF and the RTTOV fast radiative transfer code provided by the SAF on Numerical Weather Prediction, the systematic errors could be limited in the range (5% to 15%) over the full disk in the latitude range 60S to 60N.
Abe Pacini Alessandra
Drouin A.
El Amraoui L.
Ferreira V.
Karcher F.
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