1 / 15

KNMI 35 GHz Cloud Radar & Cloud Classification*

Henk Klein Baltink * Robin Hogan (Univ. of Reading, UK). KNMI 35 GHz Cloud Radar & Cloud Classification*. Outline:. 35 GHz Cloudradar: main characteristics Some examples of radar observations Cloud classification (CloudNET) Case 24 th of May 2003. Millimeter wave cloud radar Cabauw (1).

heman
Download Presentation

KNMI 35 GHz Cloud Radar & Cloud Classification*

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Henk Klein Baltink * Robin Hogan (Univ. of Reading, UK) KNMI 35 GHz Cloud Radar& Cloud Classification*

  2. Outline: • 35 GHz Cloudradar: main characteristics • Some examples of radar observations • Cloud classification (CloudNET) • Case 24th of May 2003 BBC1 Workshop

  3. Millimeter wave cloud radar Cabauw (1) • frequency: 35 GHz (8.6 mm) • peak power 100 W (TWT transmitter) • 1.8 m antenna (0.36º beam angle) • range resolution: 90 m (selectable: 45 , 150, ...) • range: 200 –13000 m (selectable) • pulsed Doppler radar • full Doppler velocity spectra BBC1 Workshop

  4. Millimeter wave cloud radar Cabauw (2) • polarisation capability on receive • pulse-coding to enhance sensitivity • flexible parameter setting (GUI) • continuous unattended operation • every 15 sec: profile of dBZe, vertical velocity,spectral width (retrieved from combination of 2 radar modes) BBC1 Workshop

  5. ARM-SGP:-54 dBZe @ 5 km Sensitivity35 GHz(BBC1,2001)2 modes:- 8-bit code (red)- uncoded (black) BBC1 Workshop

  6. Power loss over time: BBC1 Workshop

  7. After BBC1 BBC1 10000 5000 0 20 s 16 s Acquisition cycle acquisition processing Height uncoded coded coded X-pol BBC1 Workshop

  8. CT75 BACKSCATTER Coded mode before masking,.. Combined mode (database) Post-processing: • spectral analysis: • velocity unfolding • multiple peak detection • noise estimate each profile • calibration • cloud mask for each mode • insect removal • in rain: de-aliasing (uncoded only) • mode merging BBC1 Workshop

  9. liquid water? ice cloud water cloud Example Doppler spectrum Contour of Doppler Spectra radar backscatter profile range Radial velocity BBC1 Workshop

  10. Motivation for cloud classification: • Target categorization and data quality assessment initiated by CloudNET, Robin Hogan, Univ. Reading • Motivation: • many algorithms require similar pre-processing: • interpolation onto the same grid • correction of radar data for known attenuations • categorization of targets (water,ice,insects,aerosol,clutter) • assign data quality • do it once and identical for all stations BBC1 Workshop

  11. Case 24th of May 2003 • radar data • radar & lidar (ceilometer) data • target classification • comparison with RACMO BBC1 Workshop

  12. loss of signal due to raindrops on antenna(?) artefact of mode merging melting layer precip insects rain at surface Cloudradar data ice clouds (mixed?) water clouds BBC1 Workshop

  13. aerosol Radar vs. Lidar upper clouds blocked by low level clouds BBC1 Workshop

  14. target categorization & “data quality” BBC1 Workshop

  15. Comparison with RACMO cloud fraction “point value” vs. “grid box mean” BBC1 Workshop

More Related