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IAC ETH

EGU 2004, Session GI2. °. *. IAC ETH. Wind data from profilers -- how robust statistics, multi-peak analyses, and neural networks affect the overall quality Heidi Weber*, Hans Richner*, Dominique Ruffieuxº, and Ralf Kretzschmar*. standard evaluation algorithm with multiple peak addition.

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IAC ETH

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  1. EGU 2004, Session GI2 ° * IACETH Wind data from profilers -- how robust statistics, multi-peak analyses, and neural networks affect the overall quality Heidi Weber*, Hans Richner*, Dominique Ruffieuxº, and Ralf Kretzschmar*

  2. standard evaluation algorithm with multiple peak addition multiple peak algorithm

  3. Multiple peak processing, a two-step procedure (with special treatment of overlapping peaks) : (i) peak identification (ii) peak selection (chaining)

  4. example of highly contaminated spectra peak identification (X) peak selection, chaining (shaded)

  5. multiple peak processing algorithm, summary of results multiple peak processing improves data coverage (typically 10 to 20 %) more important: multiple peak processing eliminates and replaces virtually all suspicious and unrealistic wind data! algorithm is insensitive to instrument characteristics and does not need elaborate tuning algorithm can be applied in real time, hence, no time-delaying off-line reprocessing is necessary

  6. (courtesy UK Met Office) “classical” algorithm

  7. (courtesy UK Met Office) multiple peak algorithm

  8. unrealistic wind fields Heidi Weber IACETH

  9. signal-to-noise ratio

  10. neural network signal-to-noise ratio skewness signalpower kurtosis Doppler shift Heidi Weber IACETH

  11. Conclusions • elaborate algorithm significantly improve data quality • multiple peak analysis algorithms are universal • neural networks need site-specific training • there is still big potential for software improvements

  12. The End

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