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PDFs of humidity and cloud water content from Raman lidar and cloud radar Robin Hogan Ewan O’Connor Anthony Illingworth Department of Meteorology, University of Reading UK 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: Chilbolton Raman lidar – 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 Mixing ratio comparison 11 Nov 2001 Raman lidar Cloud Unified Model, Mesoscale version 12 UTC 1.6 km 15 UTC • Agreement is mixed between lidar and model: Larkhill sonde 0.8 km 0.2 km PDF comparison – 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 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 and Illingworth LONGWAVE: (GRL 2000) emissivity versus IR optical depth SHORTWAVE: albedo versus visible optical depth Carlin et al. (JClim 2002) We measure fractional variance: f IWC IWC / IWC 2 Cirrus fallstreaks and wind shear Unified Model Low shear High shear Ice water content distributions Near cloud base Cloud interior Near cloud top • 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 Vertical decorrelation • Variance at each level not enough, need vertical decorrelation/overlap info: Lower emissivity and albedo Higher emissivity and albedo • Only radar can provide this information: aircraft insufficient • Decorrelation length is a function of wind shear: – Around 700m near cloud top – Drops to 350m in fall streaks Results from 18 months of radar data Fractional variance of IWC Vertical decorrelation length Increasing shear • 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 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?