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Zhibo (zippo) Zhang 03/29/2010 ESSIC

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Presentation on theme: "Zhibo (zippo) Zhang 03/29/2010 ESSIC"— Presentation transcript:

1 Zhibo (zippo) Zhang 03/29/2010 ESSIC
Influences of ice particle model on ice cloud optical thickness retrieval Zhibo (zippo) Zhang 03/29/2010 ESSIC

2 Outline Background Influence of ice particle model on t retrieval
Importance of ice cloud Ice particle model and ice cloud retrieval Influence of ice particle model on t retrieval Comparison of MODIS and POLDER ice t retrieval Influence on our understanding of ice cloud seasonal variability Summary

3 Ice cloud: fun Photo from Wiki

4 Ice cloud: important ISCCP day-time ice cloud amount Albedo Effect
Ice clouds are important, because Cover large portion of the Earth’s surface Radiative effects Water vapor budget Cloud feedbacks ISCCP day-time ice cloud amount Earth Albedo Effect Greenhouse Effect (dominant)

5 Ice cloud: not well understood
Duane Waliser et al JGR

6 Satellite-base remote sensing of ice cloud properties
In-situ measurements Scattering model microphysics GCMs Ice Particle Model Satellite remote sensing

7 Ice particle model Size distribution Shape distribution Orientation
Inhomogeneity & surface roughness

8 Ice particle size Size matters
Cloud life time (e.g., Heymsfield 1972, Jensen et al.1996) Cloud reflectance, radiative forcing, heating/cooling rate (e.g., Ackerman et al. 1988; Jensen et al ) Cloud feedback (e.g., Stephens et al. 1990) Hard to measure Shattering of large particles Gardiner and Hallett 1985; Gayet et al. 1996 Field et al. 2003; Earth Observing Laboratory NCAR 50 µm Number density Particle Size µm mm

9 Ice particle shape Why shape also matters? Aerosol wavelength
From Bryan Baum Why shape also matters? Aerosol wavelength Ice particle wavelength Complicacy of ice particle shape must be acceptable by scattering models

10 Capabilities of current scattering models

11 How does a GOM model work?
Snell’s Law

12 Ice particle orientation
Randomly orientated Horizontally orientated Images from

13 Ice particle orientation
Horizontally orientated Image credit: CNES

14 Inhomogeneity and surface roughness
Yang et al JAMC Yang et al ITGRS

15 Ice particle model Size distribution Shape distribution Orientation
Inhomogeneity & surface roughness So many things to consider… not surprising that ice particle models are usually different from one another

16 Ice particle models: MODIS C5
More than 1000 PSDs Complicate habit/shape distribution Random orientation Homogeneous and smooth Baum et al JAMC

17 Ice particle model: MODIS C5
IWC from MODIS C5 ice particle mode is consistent with in situ measurement Baum et al JAMC Baum et al JAMC

18 Ice particle model: POLDER
Inhomogeneous Hexagonal Monocrystal Constant size (30µm) One habit only Random orientation Internal inclusion of air bubbles Courtesy of Jerome Riedi C.-Labonnote et al GRL Scattering signature consistent with POLDER observation

19 Scattering phase function Baum05 VS IHM

20 Comparison of MODIS and POLDER ice cloud retrieval
Motivation How are MODIS and POLDER ice cloud retrievals different? What is the role of ice particle model? Any implications for climate studies? Is it possible to build up a long-term ice cloud property dataset from multiple missions? MODIS POLDER Resolution 1km 20km Cloud effective radius Retrieved Assumed Ice particle model Baum05 IHM Directionality Single Up to 16 Zhang, Z.et al. 2009: Atmos. Chem. Phys., 9, 1-15. (

21 Difference between MODIS and POLDER retrieval algorithms
Resolution 1km 20km Cloud effective radius Retrieved Assumed1 Bulk scattering model Baum052 IHM3 Directionality Single Up to 16

22 Comparison of MODIS and POLDER ice cloud retrieval
POLDER/Parasol Advantages / Uniqueness : Multi-direction (up to 16 angles) Polarization sensitive (Linear polarization of cloud reflection at 3 bands) Limitations Horizontal resolution (6km) Narrow spectral coverage ( 10 bands 0.4~1.02 m) MODIS/Aqua Advantages / Uniqueness : Wide spectral coverage (36 bands 0.4 ~ 15 m) Horizontal resolution (250m ~ 1km) Limitations Single direction No polarization Column retrieval POLDER Polarization Multi-direction NASA Cloud mask, Cloud phase Cloud top height Optical thickness MODIS Effective radius

23 Case for comparison Aqua-MODIS granule on July 22, 2007 (UTC 18:45)
Flight track of TC4 mission NASA Langley TC4 team Flight track GOES IR image

24 Collocation Collocation of Level-1 radiance data
Collocation of Level-2 cloud products 6km MODIS 1km pixel 6km POLDER full resolution pixel 6km POLDER 20km downscale to 6km MODIS 1km aggregated to 6km 6km POLDER full resolution pixel

25 Same clouds; different t?
MODIS t vs POLDER t tPOLDER/tMODIS follows the log- normal distribution tPOLDER is substantially smaller than tMODIS For more than 80% pixels tPOLDER < tMODIS For more than 50% pixels tPOLDER < tMODIS by more than 30% Same clouds; different t? Why?

26 Main reason for the difference
Difference in resolution (Plane parallel albedo bias) ✗ Difference in effective radius treatment ✗ Difference in ice particle model✔ (From data: 0.68)

27 Implications for ice SW CRF
Zonal mean ice optical thickness vs month (2006)

28 Implications for ice SW CRF
Instantaneous Shortwave CRF (FSW)

29 Implications for ice SW CRF
Wrong ice particle model retrieval Wrong t retrieval FSW computation Wrong g used “Not so wrong” FSW Error cancellation

30 Ice particle model and seasonal variation of t retrieval
Difference in g Difference in higher-order moment of P11 IHM model is used for MODIS retrieval Baum05 model is used for MODIS retrieval

31 Angular signature of ice cloud reflectance
Satellite Single-scattering Multiple-scattering Angular signature is mainly determined by single-scattering

32 Bulk scattering model and seasonal variation of  retrieval
Difference in higher-order moment of P11 Difference in g

33 MODIS angular sampling
MODIS angular sampling vs season summer winter winter summer

34 Impact on seasonal variation of t retrieval
Assume IHM to be the truth winter summer

35 Summary The t of ice clouds retrieved from POLDER is substantially smaller than that from MODIS retrieval. This difference is mostly attributed to the difference in ice bulk scattering models used in MODIS and POLDER retrievals If a wrong bulk scattering model is used in the retrieval algorithm, the error in g factor may lead to overestimation or underestimation of t . However, this error in t retrieval is largely cancelled in FSW computation by the error in g factor. The error in higher-order moment of P11 may lead to artificial seasonal variation of t and this error can NOT be cancelled in FSW computation

36 Questions?

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