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Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data

Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data. Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK. Ice cloud inhomogeneity. Relationship between optical depth and emissivity.

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Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data

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  1. Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK

  2. Ice cloud inhomogeneity Relationship between optical depth and emissivity • But for ice clouds the vertical decorrelation is also important Lower emissivity Higher emissivity • Cloud infrared properties depend on emissivity • Most models assume cloud is horizontally uniform • In analogy to Sc albedo, the emissivity of non-uniform clouds is less than for uniform clouds Pomroy and Illingworth (GRL 2000)

  3. Cloud radar and ice clouds • Cloud radars can estimate ice parameters from empirical relationships with radar reflectivity, Z (liquid clouds more difficult due to drizzle). • Can evaluate gridbox-mean IWC in models, but newer models are also beginning to represent sub-grid structure • Here we use radar to estimate gridbox variances and vertical correlation of inhomogeneities We use 94-GHz Galileo radar that operates continuously from Chilbolton in Southern England

  4. Fractional variance • We quantify the horizontal inhomogeneity of ice water content (IWC) and ice extinction coefficient () using the fractional variance: • Barker et al. (1996) used a gamma distribution to represent the PDF of stratocumulus optical depth: • Their width parameter  is actually the reciprocal of the fractional variance: for p() we have  = 1/f .

  5. Deriving extinction & IWC from radar rlog logZ • But by definition, the slope of the regression line isrlog/logZ(where r is the correlation coefficient),so f is underestimated by a factor ofr2  0.45. • Regression in log-log space provides best estimate of log from a measurement of logZ(or dBZ) Use ice size spectra measured by the Met-Office C-130 aircraft during EUCREX to calculate cloud and radar parameters: =0.00342 Z0.558 IWC=0.155 Z0.693

  6. For inhomogeneity use the SD line log logZ • The “standard deviation line” has slope of log/logZ • We calculate SD line for each horizontal aircraft run • Mean expression =0.00691 Z0.841 (note exponent) • Spread of slopes indicates error in retrieved f & fIWC

  7. Cirrus fallstreaks and wind shear • This is a test … Unified Model Low shear High shear

  8. Vertical decorrelation: effect of shear • Low shear region (above 6.9 km) for 50 km boxes: • decorrelation length = 0.69 km • IWC frac. variance fIWC = 0.29 • High shear region (below 6.9 km) for 50 km boxes: • decorrelation length = 0.35 km • IWC frac. variance fIWC = 0.10

  9. 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

  10. Results from 18 months of radar data Fractional variance of IWC Vertical decorrelation length • 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 log10fIWC = 0.3log10d - 0.04s - 0.93 Increasing shear

  11. 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

  12. Conclusions • We have quantified how the fractional variances of IWC and extinction, and the vertical decorrelation, depend on model gridbox site, shear, and distance from cloud boundaries • Full expressions may be found in Hogan and Illingworth (JAS, March 2003) • Note that these expressions work well in the mean (i.e. OK for climate) but the instantaneous differences in variance are around a factor of two • Outstanding questions: • Our results are for midlatitudes: what about tropical cirrus? • Our results for fully cloudy gridboxes: How should the inhomogeneity of partially cloudy gridboxes be treated? • What other parameters affect inhomogeneity?

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