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

Second Progress Meeting21-22 October 2002, KNMI

The drizzle definition, detection and characterization

The correlation between and as

function of for different types of function .

thresholdR

drRNrY0

)()(

thresholdR

drRNrY )()(

thresholdR )(rY

Threshold value for drizzle definition:

Rmin = 17…20 m

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

thresholdCLARE’98, R =20 m

Zdr

izzl

e/ Z

drop

s, d

B

The CLARE'98 campaign data

threshold CLARE’98, R =20 m

Zdr

izzl

e/ Z

drop

s, d

B

The dependence of the ratio of drizzle reflectivity to droplets reflectivity

threshold CLARE’98, R =20 m

Zdr

izzl

e/ Z

drop

s, d

B

(a) (b)

The CLARE'98 campaign data

versus the total radar reflectivity versus the Z/ ratio

The relation between “in-situ” Effective Radius and Radar Reflectivity to Lidar Extinction Ratio

for different field campaigns.

The relation between “in-situ” Effective Radius and Radar Reflectivity to Lidar Extinction Ratio

for different field campaigns.

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

The CLARE'98 campaign data

log 10

(LW

Cdr

izzl

e, g/

m3 )

Cloud without drizzleCloud without drizzle

Cloud with light drizzleLWC < 0.05 g/m3

Cloud with light drizzleLWC < 0.05 g/m3

Cloud with heavy drizzleCloud with heavy drizzle

Radar + Lidar data: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

Application of the relation for the identification

of the Z-LWC relationship

Application of the relation for the identification

of the Z-LWC relationship

effrZ /

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

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

Re-scaling data to common grid

Re-scaling data to common grid

Zlidar(h) => (h)Zlidar(h) => (h)

Zradar(h) / (h)Zradar(h) / (h)Cloud classification map for 7 classes k(h):

0 - no cloud; 1 - Z / not available, Z < Z1 ;2 - Z / not available, Z1 < Z < Z2 ;3 - Z / not available, Z2 < Z ;4 - Z / < Q1; 5 - Q1 < Z / < Q2; 6 - Q2 < Z /.

Cloud classification map for 7 classes k(h):

0 - no cloud; 1 - Z / not available, Z < Z1 ;2 - Z / not available, Z1 < Z < Z2 ;3 - Z / not available, Z2 < Z ;4 - Z / < Q1; 5 - Q1 < Z / < Q2; 6 - Q2 < Z /.

LWC = Ak ZBkLWC = Ak ZBk

LWPZ = LWCi hiLWPZ = LWCi hi

LWPRM = ? = LWPZLWPRM = ? = LWPZ

dB301tresZ , dB20

2tresZ , 1

1Q, 8.1

2Q

The Radar, Lidar, and Radiometer datasetfrom 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, 9:30-10:30 The profiles of Optical Extinction and Radar-Lidar Ratio

The comparison of the Z-Z/ relations calculated from in-situ measured DSD and

from simultaneous radar and lidar data

Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 The Resulting Classification Map (radar and lidar data)

Cloud without drizzle Fox andIllingworth, 1997

Light drizzle Baedi et al., 2000

Heavy drizzle Drizzle Clouds

Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 Retrieval Results (classification using radar and lidar data)

Case study: August 04, 2001, Cabauw, NL, 9:30-10:30The Resulting Classification Map (only radar data)

Cloud without drizzle Fox andIllingworth, 1997

Light drizzle Baedi et al., 2000

Heavy drizzle Drizzle Clouds

Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 Retrieval Results (classification using radar data)

Frisch’s algorithm

2

log0, LWCNaZ

effr

• log-normal drop size distribution

• concentration and distribution width are equal to constant values

max

0

2/1

2/1

)(

)()(

h

h

RMMW

hZ

hZ

h

LWPhLWC

From radiometer’s LWP and radar reflectivity profile:

09:30-10:30, 04.08.2002, Cabauw, BBC-campaign

5.0)(i

ihZAhLWP

The solution of the Frisch equation

09:30-10:30, 04.08.2002, Cabauw, BBC-campaign Retrieval Results for Frisch’s algorithm

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