robin hogan julien delanoe university of reading remote sensing of ice clouds from space

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?