KNMI 35 GHz Cloud RadarKNMI 35 GHz Cloud Radar& Cloud Classification* & Cloud Classification*

Henk Klein BaltinkHenk Klein Baltink

* Robin Hogan (Univ. of Reading, UK)* Robin Hogan (Univ. of Reading, UK)

8-10 october 2003 BBC1 Workshop 2

Outline:

1.1. 35 GHz Cloudradar: main characteristics35 GHz Cloudradar: main characteristics2.2. Some examples of radar observationsSome examples of radar observations3.3. Cloud classification (CloudNET)Cloud classification (CloudNET)4.4. Case 24Case 24thth of May 2003 of May 2003

8-10 october 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

8-10 october 2003 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)

8-10 october 2003 BBC1 Workshop 5

Sensitivity35 GHz(BBC1,2001)

2 modes:- 8-bit code (red)- uncoded (black)

ARM-SGP:-54 dBZe @ 5 km

8-10 october 2003 BBC1 Workshop 6

Power loss over time:

8-10 october 2003 BBC1 Workshop 7

Acquisition cycle

0

5000

10000

20 s

BBC1

16 s

After BBC1

acquisition

processing

uncoded

coded

coded X-pol

Hei

ght

8-10 october 2003 BBC1 Workshop 8

• 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

8-10 october 2003 BBC1 Workshop 9

Example Doppler spectrum

radar backscatter profile

ice cloud

water cloud

Radial velocity

ran

ge

liquid water?

Contour of Doppler Spectra

8-10 october 2003 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

8-10 october 2003 BBC1 Workshop 11

Case 24th of May 2003

radar data radar & lidar (ceilometer) data target classification comparison with RACMO

8-10 october 2003 BBC1 Workshop 12

Clo

ud

radar

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(?)

8-10 october 2003 BBC1 Workshop 13

Radar vs. Lidar

upper clouds blockedby low level clouds

aerosol

8-10 october 2003 BBC1 Workshop 14

target categorization & “data quality”

8-10 october 2003 BBC1 Workshop 15

Comparison with RACMO cloud fraction

“point value” vs. “grid box mean”