water cloud retrievals o. a. krasnov and h. w. j. russchenberg international research centre for...
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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: [email protected], : [email protected]
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