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Cloud Resolving Model Studies of Tropical Deep Convection Observed During HIBISCUS 2004.

Cloud Resolving Model Studies of Tropical Deep Convection Observed During HIBISCUS 2004. By Daniel Grosvenor, Thomas W. Choularton, & Hugh Coe - The University of Manchester, United Kingdom With thanks to: Gerhard Held - IPMET, Brazil; Jorge Gomes – CPTEC, Brazil;

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Cloud Resolving Model Studies of Tropical Deep Convection Observed During HIBISCUS 2004.

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  1. Cloud Resolving Model Studies of Tropical Deep Convection Observed During HIBISCUS 2004. By Daniel Grosvenor, Thomas W. Choularton, & Hugh Coe - The University of Manchester, United Kingdom With thanks to: Gerhard Held - IPMET, Brazil; Jorge Gomes – CPTEC, Brazil; Andrew Robinson – UCAM, United Kingdom.

  2. Aims of Work • To simulate transport of material from lower to upper troposphere by deep convective clouds • Gases, aerosols, water vapour, ice hydrometeors • Testing and improvement of model • Comparisons to observations • GCM parameterisations • Data set for development and testing

  3. The LEM Model • Cloud Resolving Model • UK Met Office • Bulk microphysics parameterisation • 38 conversion processes between: • Vapour, liquid, rain, ice, snow, graupel. • Double moment for ice hydrometeors • Highly variable resolution:- • Boundary layer processes • Deep convection • Periodic boundary conditions

  4. 24th February, 2004 Case Study • Large squall line moving from north passes over Bauru.

  5. Model Initialisation • One sounding for whole domain • Time forcing possible • Available soundings: • 09:00 LT Campo Grande • 17:15 LT Bauru • 21:00 LT Sao Paulo • Bauru sounding fairly stable - no deep convection produced by model • Campo Grande sounding used and model forced towards Bauru sounding

  6. Model Initialisation • Squall line initialised using a warm perturbation • 2-D, 500km domain, 1km resolution • Sensitivity to aerosol concentration tested • Comparisons to radar statistics of echo tops and 3.5km CAPPI data

  7. Timeseries of max 3.5km radar reflectivity

  8. Timeseries of 3.5km radar reflectivity modes

  9. Log-Normal Distribution of 3.5km dBZ from 14:00-23:00

  10. Timeseries of Max Echotops -3 CCN = 720cm -3 CCN = 240cm Radar data Maximum of 10dBZ radar echo tops (km) Maximum of 10dBZ radar echo tops (km) Local Time

  11. Timeseries of Echotop Modes -3 CCN = 720cm -3 CCN = 240cm Radar data Mode of 10dBZ radar echo tops (km) Local Time

  12. Timeseries of Echotop Variances ) 2 Variance of 10dBZ radar echo tops (km Local Time

  13. Log-Normal Distribution of Echotops from 14:00-23:00 -0.5 -1 -1.5 -2 -2.5 Log10 of Normalised Distribution of Echo Tops -3 -3.5 -4 -4.5 -5

  14. Max Tracer at each Height as Percentage of Max Input

  15. CFC-11 tracer measurements from SF-4 (DIRAC, UCAM)

  16. Profile of Mean Liquid Water

  17. Conclusions • Echotop agreement reasonable but lack of high echotops • Simulated 3.5km dBZ generally too high – related to above? • 2-D simulations produce highly time variable statistics • Mean values hard to compare with 2-D simulations – slices through radar data should be better • Tracer outflow height close to apparent outflow from observations

  18. Future Work • Vertical radar slice comparisons (RHIs) • ECMWF/Meso model for soundings and forcing – comparisons to clouds obtained in these models • Full double moment scheme • Vertical aerosol transport • EMM (Explicit Microphysics Model)

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