1 / 22

Cloud observations now and in the near future

Retrieval algorithm formulation of cirrus microphysical properties using radar, lidar and radiometer observations applicable to satellite, airborne and ground-based platforms.

cecil
Download Presentation

Cloud observations now and in the near future

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. Retrieval algorithm formulation of cirrus microphysical properties using radar, lidar and radiometer observations applicable to satellite, airborne and ground-based platforms Yuying Zhang, Jay Mace (University of Utah), Ping Yang (Texas A&M), Gerry Heymsfield, Matthew McGill, and Lihua Li (NASA GSFC) Objective: Demonstrate a suite of cirrus cloud property retrieval algorithms that can be applied to the A-Train.

  2. The A-Train ?/05 ?/07 12/02 4/05 1/04 Slide Courtesy Graeme Stephens Cloud observations now and in the near future We will use data from cloudsat, calipso, and Aqua MODIS…..

  3. CloudSat Radar  Millimeter radar (-28 dBZe min signal)  Provide 250 m Vertical profile of Ze Deriving Vis Tau from Lidar: • Optical lidar (Penetrates ~3 optical depth) • Vertical profile of attenuated backscatter CALIPSO Aqua MODIS  Passive radiometer scattered and emitted radiance  Integral constraint Due to radar sensitivity, ER2 data suggest that the data concurrence of the active sensors will be minimal! By time the radar is sensing the layer, the lidar is approaching the attenuation limit.

  4. II. Retrieval algorithms X X X X X X X X X

  5. II. Retrieval algorithms 2. Forward model Qabs fitted Yang et al. 2003 empirical relation

  6. IV. Case Study CRYSTAL-FACE July 26 2002 3 2 1

  7. Courtesy G.Heymsfield Courtesy G.Heymsfield& M. McGill Courtesy G.Heymsfield& S. Platnick

  8. Avalone et al. Weinstock et al. 2002

  9. IV. Case Study CRYSTAL-FACE July 29 2002

  10. IV. Case Study CRYSTAL-FACE July 29 2002

  11. IV. Summary • A major advantage of the A-Train: use multiple data streams for cloud property retrieval Goal: develop an algorithm suite to exploit this resource and mitigate the instrument sensitivity issues • From IWC comparison with WB57, the lidar-radiometer algorithm is able to retrieve reliable microphysical properties • Retrieval algorithms are sensitive to empirical constants • In future, use radar and lidar profiles to retrieve vertical structure of cirrus clouds

  12. Aqua MODIS  Passive radiometer scattered and emitted radiance  Integral constraint  Algorithm development Radiance  emissivity CO2 channel (Wylie and Menzel, 1989; Wylie et al., 1994; Liou, 2002)

  13. CloudSat Radar  Millimeter radar  Vertical profile of Ze

  14. Importance of cirrus energy budget of the Earth-atmosphere system 1. reflect solar radiation hydrological cycle of Earth-atmosphere system cool Earth-atmosphere system warm contain IR energy greenhouse effect (warming) dominates over albedo effect (cooling) for optically thin cirrus over a warm surface 2.

  15. CALIPSO • Optical lidar • Vertical profile of attenuated backscatter Lidar signal  cloud layer transmissivity (Mitrescu and Stephens, 2002; Young 1995) Height (km) Lidar signal

  16. II. Retrieval algorithms 3. Optimal estimation framework (Rodgers, 1976; Rodgers, 2002)

  17. III. Sensitivity

  18. III. Sensitivity

  19. III. Sensitivity

  20. III. Sensitivity

  21. MOD06 Cloud Products compare with lidar-Radiometer retrievals MOD06 (King et al. 1997) retrieval Optical thickness

More Related