1 / 22

Water cloud retrievals

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,

jadon
Télécharger la présentation

Water cloud retrievals

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. The drizzle definition, detection and characterization

  3. The correlation between and as • function of for different types of function . Threshold value for drizzle definition: Rmin = 17…20 mm

  4. , 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

  5. , 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

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

  7. 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

  8. 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

  9. Application of the relation for the identification of the Z-LWC relationship

  10. 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

  11. 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.

  12. Case study: August 04, 2001, Cabauw, NL, 9.00-12.00 The profiles of measured variables

  13. Case study: August 04, 2001, Cabauw, NL, 9:30-10:30 The profiles of Optical Extinction and Radar-Lidar Ratio

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

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

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

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

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

  19. 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:

  20. 09:30-10:30, 04.08.2002, Cabauw, BBC-campaign The solution of the Frisch equation

  21. 09:30-10:30, 04.08.2002, Cabauw, BBC-campaignRetrieval Results for Frisch’s algorithm

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

More Related