Presentation is loading. Please wait.

Presentation is loading. Please wait.

Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data.

Similar presentations


Presentation on theme: "Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data."— Presentation transcript:

1 Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data.

2 2 sites: coastal Europe, 1 site: Southern great plains; USA GOAL : Compare and evaluate microphysical cloud properties at 3 sites 1. Cabauw: ECMWF data (T & P) 35 GHz Radar 2. Chilbolton: ECMWF data (T & P) 94 GHz Radar (Galileo) 905 nm CT-75 Ceilometer 3. ARM: Sonde data (T & P) 35 GHz Radar 532 nm Micro Pulse Lidar (MPL) 905 nm CT-75 Ceilometer Site Instrument Period Oct 2001 – March 2002 Oct 2001 – June 2003 Due to problems with Galileo only Z> -20 dB is used Nov/Dec 1996 June 1997 Jan & July 2000

3 Selection of cloud typed 1.Ice clouds 2.Visible in both Lidar and Radar T < -2 o C Radar and Lidar top of cloud are roughly the same. Ice-clouds are optically thin. Particle sizes (R’eff) Only Lidar Both Radar and Lidar Only Radar 24-05-200211-03-2002

4 Selection of cloud typed 1.Ice clouds 2.Visible in both Lidar and Radar T < -2 o C Radar and Lidar top of cloud are roughly the same. Ice-clouds are optically thin. Particle sizes (R’eff) EXCLUDED INCLUDED

5 (11 year mean of the month June, HIRS data NOAA) Wylie & Menzel (1998) High Cloud Statistics: Frequency of cumulated IR transmissive clouds above 4 km.

6 Comparing vertical cloud statistics at the three sites. Observed low  clouds ARM Cabauw Chilbolton Shown is the normalized cloud height distribution FOR EACH cloud pixel detected

7 Example for an ice-cloud measured at Cabauw.

8 How to deal with the observed clouds Define the regions wherein 10, 30, 60, 90 and 99 % of all observed values reside Calculate the mean in each x-bin (  T) and the  of the distribution Plot for every cloud pixel the appropriate values (T vs R’ eff )

9 ARM vs CABAUW  vs. Z Doppler velocity vs. R’eff R’eff vs. T IWC’ vs Z

10 Depth from top of cloud vs. Size IWC’ distribution Height vs. Size ARM vs CABAUW Height vs. particle size Reff vs T (complex poly- crystals)

11 Need to use data with Z > -20 dBz for comparison with The GALILEO radar in the 2001-2002 period. Dependence of the retrieved particle sizes on Z. Height dist. of the probed clouds (with lower limit to used Z data) Particle size versus Temperature (with lower limit to used Z data)

12 Z > -20 dB -Cabauw -ARM -Chilbolton Depth from top of cloud vs. Size IWC vs. Z (complex poly- crystals) Height vs. particle size R’eff vs. T

13 Seasonal influences on the low optical depth ice clouds WINTER SPRING SUMMER AUTUMN HEIGHT R’eff HEIGHT Log 10 (IWC’) R’eff Bottom row: ARM Top row: Cabauw Log 10 (IWC’)

14 CONCLUSIONS 4. The cabauw site shows no seasonal dependence for the low optical depth ice-clouds studied here. The ARM site shows a small dependence. 1.Cabauw & Chilbolton show very similar results (for Z > -20 dBz) 2.Derived parameter relations depend strongly on the lowest value of Z for Z > -30 dBz 3. The ARM site has higher and thicker ice-clouds the latter results in a larger particle size distribution.

15 THANKS DAVE !!! For questions or comments: ask Dave or contact me: zadelhof@knmi.nl THE END !


Download ppt "Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data."

Similar presentations


Ads by Google