robin hogan ewan oconnor anthony illingworth department of meteorology, university of reading uk...
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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?