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Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.

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Presentation on theme: "Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space."— Presentation transcript:

1 Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space

2 Overview New variational radar-lidar-radiometer retrieval for ice clouds –Use of a-priori knowledge of the vertical distribution of ice cloud properties to spread information vertically Statistics from a month of CloudSat-CALIPSO-MODIS data –Global coverage from polar-orbiting satellites –Preliminary comparison with the Met Office model Spectral analysis to reveal the spatial structure of cirrus clouds from 1 km to 1000 km –Whats the difference between tropical & mid-latitude cirrus? –What determines the variation of power spectra with height? –Can it be represented in a fractal cirrus model?

3 Variational retrieval method Advantages of combining radar, lidar and radiometers –Radar Z D 6, lidar D 2 so the combination provides particle size –Include radiances to ensure that the retrieved profiles can be used for radiative transfer studies How the variational approach works –Define the state vector x as a profile of two parameters of the size distribution (e.g. extinction coefficient and normalized number concentration parameter N 0 *) –Iteratively find the x that best forward models the observations Key advantages –Can include any number/type of observations –Can blend smoothly between regions where both radar and lidar detect the cloud to where only one is sensitive –But need a good a priori for how cloud properties change in the vertical Delanoe and Hogan (JGR 2008)

4 CloudSat-CALIPSO-MODIS example Lidar observations Radar observations 1000 km

5 CloudSat-CALIPSO-MODIS example Lidar observations Lidar forward model Radar observations Radar forward model

6 Extinction coefficient Ice water content Effective radius Forward model MODIS m observations Radar-lidar retrieval

7 Radiances matched by increasing extinction near cloud top …add infrared radiances Forward model MODIS m observations

8 How to spread information in height Delanoe and Hogan (JGR 2008) Results from a large in-situ database: –Climatologically, N 0 */ 0.6 varies with temperature independent of IWC –We can use this as an a-priori Is this due to aggregation? –Number of large particles reduces with depth, but mass flux roughly constant? –Implies a vertical error correlation in this quantity, implemented via a B matrix But most clouds are not all seen by both radar and lidar –Radar can miss the tenuous tops, lidar extinguished before the base –Need to spread information vertically from radar-lidar region to radar-only and lidar-only regions of the cloud

9 One orbit in July 2006

10 A-Train Model Comparison with Met Office model log10(IWC[kg m -3 ]) Antarctica Central Pacific Arctic Ocean Central Atlantic South Atlantic Russia

11 July 2006 global comparison Too little spread in model ECMWF coming soon! Temperature (˚C) Model A-Train

12 Northern (summer) hemisphere –IWC concentrated at warmer temperatures Southern (winter) hemisphere –IWC concentrated at colder temperatures

13 First comparison with ECMWF log10(IWC[kg m -3 ])

14 Mean effective radius July 2006 mean value of r e =3IWP/2 i Just the top 500 m of cloud MODIS/Aqua

15 Effective radius versus temperature All clouds An effective radius parameterization?

16 Ice water pathOptical depth Mean of all skies Mean of clouds MODIS/Aqua

17 Structure of Southern Ocean cirrus Observations -Note limitations of each instrument Retrievals

18 -5/3: Cloud-top turbulence & upscale cascade Fall-streaks & wind- shear remove smaller scales lower in cloud: steeper power spectra Hogan and Kew (QJ 2005) Outer scale km –Slice through Hogan & Kew 3D fractal cirrus model –Southern Ocean cirrus is just like Chilbolton cirrus! 90 km

19 Tropical Indian Ocean cirrus Stratiform region in upper half of cloud? Turbulent fall- streaks in lower half of cloud? BurmaIndian Ocean


21 120 km Stratiform upper region dominated by larger scales Turbulent lower region 600 km –Sum of two fractal cirrus simulations –Fall-streak paradigm unsuitable for cloud top

22 3D structure Simulated Observed We can validate the 3D structure using the MODIS infrared window channel image …not very similar!

23 Summary and future work New dataset provides a unique perspective on global ice clouds Planned retrieval enhancements –Retrieve liquid clouds and precipitation at the same time –Incorporate microwave and visible radiances –Adapt for EarthCARE satellite (launch 2013) Model evaluation –Global forecast models (Met Office and ECMWF): IWC and r e –High-resolution simulations of tropical convection in CASCADE Cloud structure and microphysics –What is the explanation for the different regions in tropical cirrus (e.g. Brewer-Dobson-driven ascent in the TTL)? –What determines the outer scale of variability? –Can we represent tropical cirrus in the Hogan & Kew fractal model? –Can we resolve the small crystal controversy?

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