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Drizzle rates inferred from CloudSat & CALIPSO compared to their representation in the operational

Met Office and ECMWF forecast models.

Lee Hawkness-Smith and Anthony Illingworth

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MethodMethod Isolate clouds with tops warmer than 0° C. No ice above.

1. Estimate LWP: Attenuation of surface return (day and night)

MODIS (day time only)

2. Separate Z(OBSERVED) Z(CLOUD) & Z(DRIZLE):

Lidar gives cloud top assume adiabatic or subadiabatic profile.

Predict Z profile from this LWC.

Map Z(CLOUD) on to the CloudSat gate resolution.

Z(DRIZZLE) = Z(OBSERVED) – Z(CLOUD). {Z(CLOUD) v low}

3. Identify model clouds with tops warmer than 0°C and no ice above:

(a) Average observed drizzle rates onto model gridboxes.

(b) Forward model Z from ECMWF model ‘rain’ flux.

4. Possible explanation for differences?

Fraction of clouds which are drizzling as f(LWP): Fraction of clouds which are drizzling as f(LWP): Compare ECMWF to gridbox averaged observations Compare ECMWF to gridbox averaged observations

Observations

100 g/m2 3

Model

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Fraction of clouds which are drizzling as f(LWP): Fraction of clouds which are drizzling as f(LWP): Compare Met Office global model to gridbox averaged obs.Compare Met Office global model to gridbox averaged obs.

Observations

Model

Observations: Z - LWPLWP 100 g/m2 -20dBZ

OBSERVEDZ

LWP

ECMWF forward model:LWP 100 g/m2 0dBZ

100 times too much drizzle!Drizzle rate 0.03mm/hr

MODEL

Compare ECMWF forward Compare ECMWF forward model to observationsmodel to observations

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Evidence that the clouds in ECMWF are more Evidence that the clouds in ECMWF are more adiabatic than observed?adiabatic than observed?

F

Observed 25% adiabatic? Modelled 50% adiabatic?

MODEL AUTOCONVERSION:100g/m 2: 100% adiabatic 0.03mm/hr 0dBZ

50% adiabatic 0.02mm/hr25% adiabatic 0.01mm/hr -8dBZ

PDFs of MODIS and ECMWF dilution PDFs of MODIS and ECMWF dilution coefficients for cloud fraction > 50%coefficients for cloud fraction > 50%