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 , Fax: : Second Progress Meeting October 2002, KNMI

The drizzle definition, detection and characterization

The correlation between and as function of for different types of function. Threshold value for drizzle definition: R min = 17…20  m

The dependence between the ratio of drizzle to droplets reflectivities versus the ratio of drizzle to droplets LWCs threshold  CLARE’98,R=20m Z drizzle / Z drops, dB The CLARE'98 campaign data

threshold  CLARE’98,R=20m Z drizzle / Z drops, dB The dependence of the ratio of drizzle reflectivity to droplets reflectivity threshold  CLARE’98,R=20m Z drizzle / Z drops, dB (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 dependence of the LWC in drizzle fraction versus the Z/  ratio. The CLARE'98 campaign data log 10 (LWC drizzle, g/m 3 ) Cloud without drizzle Cloud with light drizzle LWC < 0.05 g/m 3 Cloud 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

The algorithm for the water cloud LWC retrieval from simultaneous radar and lidar measurements Re-scaling data to common grid Z lidar (h) =>  (h) Z radar (h) /  (h) Cloud classification map for 7 classes k(h): 0 - no cloud; 1 - Z /  not available, Z < Z 1 ; 2 - Z /  not available, Z 1 < Z < Z 2 ; 3 - Z /  not available, Z 2 < Z ; 4 - Z /  < Q 1 ; 5 - Q 1 < Z /  < Q 2 ; 6 - Q 2 < Z / . Cloud classification map for 7 classes k(h): 0 - no cloud; 1 - Z /  not available, Z < Z 1 ; 2 - Z /  not available, Z 1 < Z < Z 2 ; 3 - Z /  not available, Z 2 < Z ; 4 - Z /  < Q 1 ; 5 - Q 1 < Z /  < Q 2 ; 6 - Q 2 < Z / . LWC = A k Z Bk LWP Z =  LWC i  h i LWP RM = ? = LWP Z

The Radar, Lidar, and Radiometer dataset from 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, 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)

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, 9:30-10:30 The Resulting Classification Map (only radar data)

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

Frisch’s algorithm log-normal drop size distribution concentration and distribution width are equal to constant values From radiometer’s LWP and radar reflectivity profile:

09:30-10:30, , Cabauw, BBC-campaign The solution of the Frisch equation

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

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