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

2. 8-10 october 2003BBC1 Workshop2 Outline: 1.35 GHz Cloudradar: main characteristics 2.Some examples of radar observations 3.Cloud classification (CloudNET) 4.Case 24 th of May 2003

3. 8-10 october 2003BBC1 Workshop3 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

4. 8-10 october 2003BBC1 Workshop4 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)

5. 8-10 october 2003BBC1 Workshop5 Sensitivity 35 GHz (BBC1,2001) 2 modes: - 8-bit code (red) - uncoded (black) ARM-SGP: -54 dBZe @ 5 km

6. 8-10 october 2003BBC1 Workshop6 Power loss over time:

7. 8-10 october 2003BBC1 Workshop7 Acquisition cycle 0 5000 10000 20 s BBC1 16 s After BBC1 acquisition processing uncoded coded coded X-pol Height

8. 8-10 october 2003BBC1 Workshop8 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 Post-processing: Coded mode before masking,..Combined mode (database) CT75 BACKSCATTER

9. 8-10 october 2003BBC1 Workshop9 Example Doppler spectrum radar backscatter profile ice cloud water cloud Radial velocity range liquid water? Contour of Doppler Spectra

10. 8-10 october 2003BBC1 Workshop10 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

11. 8-10 october 2003BBC1 Workshop11 Case 24th of May 2003 radar data radar & lidar (ceilometer) data target classification comparison with RACMO

12. 8-10 october 2003BBC1 Workshop12 Cloudradar data rain at surface melting layer artefact of mode merging ice clouds (mixed?) water clouds precip insects loss of signal due to raindrops on antenna(?)

13. 8-10 october 2003BBC1 Workshop13 Radar vs. Lidar upper clouds blocked by low level clouds aerosol

14. 8-10 october 2003BBC1 Workshop14 target categorization & “data quality”

15. 8-10 october 2003BBC1 Workshop15 Comparison with RACMO cloud fraction “point value” vs. “grid box mean”