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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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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
The correlation between and as • function of for different types of function . Threshold value for drizzle definition: Rmin = 17…20 mm
, dB 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
, dB , 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
The relation between “in-situ” Effective Radius and Radar Reflectivity to Lidar Extinction Ratio for different field campaigns.
log10(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
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
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
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/arelations 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, 9:30-10:30Retrieval Results (classification using radar and lidar data)
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30The Resulting Classification Map (only radar data)
Case study: August 04, 2001, Cabauw, NL, 9:30-10:30Retrieval 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, 04.08.2002, Cabauw, BBC-campaign The solution of the Frisch equation
09:30-10:30, 04.08.2002, Cabauw, BBC-campaignRetrieval Results for Frisch’s algorithm
Difference between LWC that retrieved using Frisch method and retrieved from radar-to-lidar ratio