Robin HoganRobin HoganJulien DelanoeJulien DelanoeUniversity of ReadingUniversity of Reading

Remote sensing of ice Remote sensing of ice clouds from spaceclouds from space

OverviewOverview• 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– What’s 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?

Variational retrieval method Variational retrieval method • Advantages of combining radar, lidar and radiometers

– Radar ZD6, lidar ’D2 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” N0*)

– 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)

CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample

• Lidar observations

• Radar observations

1000 km

CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample

• Lidar observations

• Lidar forward model

• Radar observations

• Radar forward model

• Extinction coefficient

• Ice water content

• Effective radius

Forward modelMODIS 10.8-m observations

Radar-lidar retrievalRadar-lidar retrieval

Radiances matched by increasing extinction near cloud top

……add infrared radiancesadd infrared radiances

Forward modelMODIS 10.8-m observations

How to spread information in How to spread information in heightheight

Delanoe and Hogan (JGR 2008)

• Results from a large in-situ database:– Climatologically, N0*/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

One orbit in July 2006

A-Train

Model

Comparison with Met Office Comparison with Met Office modelmodel

log10(IWC[kg m-3])

Antarctica

CentralPacific

ArcticOcean

CentralAtlantic

SouthAtlantic

Russia

July 2006 global comparisonJuly 2006 global comparison

• Too little spread in model

• ECMWF coming soon!

Tem

pera

ture

(˚C

)

Model A-Train

Mean effective radiusMean effective radius• July 2006 mean value of

re=3IWP/2i

• Just the top 500 m of cloud

• MODIS/Aqua

Effective radius versus Effective radius versus temperaturetemperature

All clouds

An effective radius parameterization?

Ice water pathIce water path Optical Optical depthdepth• Mean of all skies

• Mean of clouds

• MODIS/Aqua

Structure of Southern Ocean Structure of Southern Ocean cirruscirrus

Observations- Note limitations

of each instrument

Retrievals

-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 50-100 km

– Slice through Hogan & Kew 3D fractal cirrus model

– Southern Ocean cirrus is just like Chilbolton cirrus!

90 km

Tropical Tropical Indian Ocean Indian Ocean

cirruscirrus

Stratiform region in upper half of cloud?

Turbulent fall-streaks in lower half of cloud?

BurmaIndian Ocean

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

Simulated

Observed

• We can validate the 3D structure using the MODIS infrared window channel image

…not very similar!

Summary and future workSummary and future workNew 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 re

– 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?