Robin HoganEwan O’Connor

Anthony IllingworthDepartment of Meteorology, University of Reading UK

PDFs of humidity and cloud water content from Raman lidar and cloud radar

Sub-gridscale structure in GCMs• Small-scale structure in GCMs can have

large scale effects:– Sub-grid humidity distribution used to

determine cloud fraction (e.g. in UM)– Sub-grid cloud water distribution affects mean

fluxes (crudely represented in ECMWF, not in UM)

• We use radar and lidar to make high-resolution measurements of water vapour and cloud content:– Raman lidar provides water vapour mixing

ratio from ratio of the water vapour and nitrogen Raman returns

– Empirical relationships provide ice water content from radar reflectivity

• Liquid clouds are more tricky!

Chilbolton cloud radar

Chilbolton Raman lidar

Mixing ratio comparison 11 Nov 2001

Ramanlidar

UnifiedModel,Mesoscaleversion

Cloud

PDF comparison• Agreement is mixed

between lidar and model:– Good agreement at low levels– Some bimodal PDFs in the

vicinity of vertical gradients

• Further analysis required:– More systematic study– Partially cloudy cases with

PDF of liquid+vapour content

12 UTC 15 UTC

1.6 km

0.2 km

0.8 km

Larkhillsonde

Smith (1990) triangular PDF

scheme

Ice cloud inhomogeneity• Most models assume cloud is horizontally uniform• Non-uniform clouds have lower emissivity & albedo

for same mean due to curvature in the relationships

Pomroy andIllingworth(GRL 2000)LONGWAVE:

emissivity versus

IR optical depth

SHORTWAVE: albedo versus visible optical

depth

Carlin et al.(JClim 2002)

We measure fractional variance: 2/ IWCf IWCIWC

Cirrus fallstreaks and wind shear

Low shear

High shear

Unified Model

Ice water content distributions

• PDFs of IWC within a model gridbox can often, but not always, be fitted by a lognormal or gamma distribution

• Fractional variance tends to be higher near cloud boundaries

Near cloud base Cloud interior Near cloud top

• Variance at each level not enough, need vertical decorrelation/overlap info:

• Only radar can provide this information: aircraft insufficient

Vertical decorrelation

• Decorrelation length is a function of wind shear:– Around 700m near cloud top– Drops to 350m in fall streaks

Lower emissivity and albedo

Higher emissivity and albedo

Results from 18 months of radar data

• Variance and decorrelation increase with gridbox size– Shear makes overlap of inhomogeneities more random, thereby

reducing the vertical decorrelation length– Shear increases mixing, reducing variance of ice water content

– Can derive expressions such as log10 fIWC = 0.3log10d - 0.04s - 0.93

Fractional variance of IWC Vertical decorrelation length

Increasing shear

Distance from cloud boundaries

• Can refine this further: consider shear <10 ms-1/km

– Variance greatest at cloud boundaries, at its least around a third of the distance up from cloud base

– Thicker clouds tend to have lower fractional variance– Can represent this reasonably well analytically

Conclusions• We have quantified how fractional variances of IWC

and extinction, and the vertical decorrelation, depend on model resolution, shear etc.– Full expressions in Hogan and Illingworth (JAS, March 2003)– Expressions work well in the mean (i.e. OK for climate) but

the instantaneous differences in variance are around a factor of two

• Raman lidar shows great potential for evaluating model humidity field

• Outstanding questions:– Our results are for midlatitudes: what about tropical cirrus?– What other parameters affect inhomogeneity?– What observations could be used to get the high resolution

vertical and horizontal structure of liquid water content?