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Second Progress Meeting 21-22 October 2002, KNMI. Water cloud retrievals. O. A. Krasnov and H. W. J. Russchenberg International Research Centre for Telecommunications-transmission and Radar, Faculty of Information Technology and Systems, Delft University of Technology,

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Water cloud retrievals

Second Progress Meeting21-22 October 2002, KNMI

Water cloud retrievals

O. A. Krasnov and H. W. J. Russchenberg

International Research Centre for Telecommunications-transmission and Radar,

Faculty of Information Technology and Systems, Delft University of Technology,

Mekelweg 4, 2628 CD Delft, The Netherlands.

Ph. +31 15 2787544, Fax: +31 15 2784046

E-mail: o.krasnov@irctr.tudelft.nl, : h.w.j.russchenberg@irctr.tudelft.nl



Water cloud retrievals

Threshold value for drizzle definition:

Rmin = 17…20 mm


Water cloud retrievals

, dB as

drops

Z

/

drizzle

m

CLARE’98,

R

=20

m

Z

threshold

The dependence between the ratio of drizzle to droplets reflectivities versus the ratio of drizzle to droplets LWCs

The CLARE'98 campaign data


Water cloud retrievals

, dB as

, dB

drops

drops

Z

Z

/

/

drizzle

drizzle

Z

m

Z

m

CLARE’98,

CLARE’98,

R

R

=20

=20

m

m

threshold

threshold

The dependence of the ratio of drizzle reflectivity to droplets reflectivity

versus the total radar reflectivity

versus the Z/a ratio

(a)

(b)

The CLARE'98 campaign data


Water cloud retrievals

The relation between “in-situ” Effective Radius and Radar Reflectivity to Lidar Extinction Ratio for different field campaigns.


Water cloud retrievals

log Radar Reflectivity to Lidar Extinction Ratio 10(LWCdrizzle, g/m3)

The dependence of the LWC in drizzle fraction versus the Z/a ratio.

Cloud without drizzle

Cloud with heavy drizzle

Cloud with light drizzleLWC < 0.05 g/m3

The CLARE'98 campaign data


Water cloud retrievals
Radar + Lidar data: Radar Reflectivity to Lidar Extinction Ratio LWC retrieval algorithm,based on the classification of the cloud’s cells into three classes:

  • cloud without drizzle,

  • cloud with light drizzle,

  • cloud with heavy drizzle


Water cloud retrievals

Application of the relation for the identification

of the Z-LWC relationship


Water cloud retrievals

The algorithm for the water cloud LWC retrieval from simultaneous radar and lidar measurements

Re-scaling data to common grid

Zlidar(h) => a (h)

  • Cloud classification map for

  • 7 classes k(h):

    • 0 - no cloud;

    • 1 - Z /a not available, Z < Z1 ;

    • 2 - Z /a not available, Z1 <Z < Z2 ;

    • 3 - Z /a not available, Z2 <Z ;

    • 4 - Z /a < Q1;

    • 5 - Q1 < Z /a < Q2;

    • 6 - Q2 < Z /a.

Zradar(h) / a (h)

LWC = Ak ZBk

LWPZ = S LWCiD hi

LWPRM = ? = LWPZ


Water cloud retrievals
The Radar, Lidar, and Radiometer dataset simultaneous radar and lidar measurementsfrom the Baltex Bridge Cloud (BBC) campaign August 1- September 30, 2001, Cabauw, NL

  • Radar Reflectivity from the 95 GHz Radar MIRACLE (GKSS)

  • Lidar Backscattering Coefficient from the CT75K Lidar Ceilometer (KNMI)

  • Liquid Water Path from the 22 channel MICCY (UBonn)

    All data were presented in equal time-height grid with time interval 30 sec and height interval 30 m.


Case study august 04 2001 cabauw nl 9 00 12 00 the profiles of measured variables
Case study: August 04, 2001, Cabauw, NL, 9.00-12.00 simultaneous radar and lidar measurements The profiles of measured variables


Water cloud retrievals
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 simultaneous radar and lidar measurementsThe profiles of Optical Extinction and Radar-Lidar Ratio


Water cloud retrievals

The comparison of the simultaneous radar and lidar measurementsZ-Z/arelations calculated from in-situ measured DSD and from simultaneous radar and lidar data


Water cloud retrievals
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 simultaneous radar and lidar measurementsThe Resulting Classification Map (radar and lidar data)


Water cloud retrievals
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 simultaneous radar and lidar measurementsRetrieval Results (classification using radar and lidar data)


Case study august 04 2001 cabauw nl 9 30 10 30 the resulting classification map only radar data
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 simultaneous radar and lidar measurementsThe Resulting Classification Map (only radar data)


Case study august 04 2001 cabauw nl 9 30 10 30 retrieval results classification using radar data
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 simultaneous radar and lidar measurementsRetrieval Results (classification using radar data)


Frisch s algorithm
Frisch’s algorithm simultaneous radar and lidar measurements

  • log-normal drop size distribution

  • concentration and distribution width are equal to constant values

From radiometer’s LWP and radar reflectivity profile:


Water cloud retrievals

09:30-10:30, 04.08.2002, Cabauw, BBC-campaign simultaneous radar and lidar measurements

The solution of the Frisch equation


Water cloud retrievals

09:30-10:30, 04.08.2002, Cabauw, BBC-campaign simultaneous radar and lidar measurementsRetrieval Results for Frisch’s algorithm


Difference between lwc that retrieved using frisch method and retrieved from radar to lidar ratio
Difference between LWC that retrieved using Frisch method and retrieved from radar-to-lidar ratio