John Marsham 1 , Steven Dobbie 1 and Robin Hogan 2

1. Large eddy model simulations of lidar and Doppler radar data from a mixed phase cloud: constraining vertical velocities and fallspeeds. John Marsham1, Steven Dobbie1 and Robin Hogan2 1Institute for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK 2Department of Meteorology, University of Reading, Reading, UK • Summary • There are significant differences between results from different cloud resolving models (CRMs) for ice clouds1 and few published simulations of mixed phase layer clouds and so a need to compare CRM results with observations. Using the Met Office LEM, coupled to the Fu-Liou radiation scheme2-5, we simulate lidar and Doppler radar data from an altocumulus cloud observed by the Chilbolton 94 GHz radar and 905 nm lidar on the 5th September 2003 (Figure 1). Simulating the observations allowed an accurate comparison between the LEM and the radar and lidar data and also allows us to investigate relationships between observed and unobserved parameters(e.g. standard deviations in Doppler velocities & vertical winds). • The LEM captures the ice structures well, but gives more ice than observed (Figures 1 & 2). The LEM shows turbulence and super-cooled liquid water at cloud-top as observed (Figures 1 & 2), although retrievals from a dual wavelength microwave radiometer show that there are too few liquid water cells in the model (Figure 3). • The simulated radar data shows that: (i) the standard deviation in mean Doppler velocities, s(VD), gives an accurate estimate of the standard deviation in vertical winds, s(w) (Figure 4) and (ii) large values of s(VD) tend to occur for low IWC at the edges of convecting cells (Figure 5). • The LEM over-estimates the mass-squared-weighted fallspeeds(Figures 6 & 7). Changing the modelled fallspeeds to fit the observations still gives a realistic cloud, increasing the LWP by ~10% and the IWC by a factor ~1.5 (not shown). • Many numerical weather prediction models failed to give a good forecast for this case. The results show theimportance of using a high vertical resolution to capture the thin layer of liquid water and representing the vertical velocities that allow liquid water to form and suggest that separate prognostic ice-nuceli are also required. = Radiosonde • Figure 1: Observed and simulated data. The LEM was initialised with the 5 UTC Larkhill radiosonde (~20 km from Chilbolton). The horizontally averaged temperature and water vapour mixing ratio were then relaxed towards profiles from radiosondes at 8, 10 and 12 UTC. The mean windspeed profile from the four radiosondes was used. • IWC were output from the LEM to mimic the sampling of the radar.The Doppler velocity is given by: VD = w + Vz (w is the vertical wind and Vz is the mass-squared-weighted fallspeed). The attenuated lidar backscatter was calculated from the extinction coefficients of the hydrometeor species, using an extinction-to-backscatter ratio of 18.5 sr for ice and water6. • The LEM captures the ice structures well, but the IWC is too high and the cloud-top height varies too little. • The LEM gives realistic turbulence (i.e.s(VD)) at the cloud-top, but too little at lower levels. • Mean Doppler velocities in the LEM are larger than observed, since LEM fallspeeds are larger (Figures 6 & 7). Wave motions also appear to propagate against the mean-flow, so the time-averaged vertical velocity is not equal to zero at all heights (clearest at ~ 4.5km). This effect is not caused by the relaxation method, or the wind-shear profile used and is not removed by damping vertical velocities and potential temperature perturbations at the edge of the domain. • Liquid water forms at the cloud-topin reality and in the LEM (and also some at the cloud-base). The ice-backscatter is much larger in the LEM than in the observations:(i) the sensitivity of this low power lidar ceilometer is poor (ii) the extinction-to-backscatter ratio for ice may have been over-estimated. Observed Simulated IWC IWC s(VD) s(VD) VD VD Lidar Lidar Figure 2: There is two to three times more ice in the LEM than observed.The LEM captures the increased turbulence at the cloud-top, which allows the LWC to form (this maximum is more sharply peaked in the LEM than the observations, since the cloud-top is less variable in the LEM). The LEM gives less turbulence within the cloud and at cloud-base and so less LWC than observed at cloud-base (although there is still a peak in s(VD) here). Figure 5: The modelled bi-variate pdf of IWC and s(VD) is similar to the pdf observed. Larger values of s(VD)) are found for smaller values of IWC – at the edge of convecting cells near the cloud-top. Figure 3: The LWP. The maximum LWP observed in a single column of the LEM is comparable with the microwave values, whilst the mean LWP in the LEM is much lower than observed – the LEM is producing significantly fewer liquid water cells than occurred in reality (although the LWP of the liquid water cells in the LEM is close to that observed). It is possible that using prognostic ice nuclei (IN) would allow more liquid water to form in the model, since then ice nucleation, growth and fallout could reduce IN concentrations7. Figure 7: Pdfs for data from between 3 km and 8 km. There is little dependence of VD or VZ on IWC. Ice growing at the cloud-top and sublimating at the cloud-base gives a bi-modal distribution of VZ for low IWC in the LEM. Figure 6: Time averaged LEM mass-squared-weighted terminal fallspeeds & radar Doppler velocities. Modelled mass-squared weighted fallspeeds are 1.5 times larger than observed. Figure 4: In this cloud s(VD) provides an almost unbiased estimate of s(w) with a small random error. Acknowledgments:The authors would like to acknowledge the Cloudnet project (EU project EUK2-2000-00611) for radar, lidar and radiometer data from the Chilbolton Facility for Atmospheric and Radio Research (part of the Rutherford Appleton Laboratory) and the NWP model output. Nicolas Gaussiat performed the microwave radiometer retrieval. Radiosonde data were provided by BADC. This work was funded by the Natural Environment Research Council (NERC: NER/M/S/2002/00127). References:(1) D. O’ C. Starr, A. Benedetti, M. Boehm, P. A. Brown, K. M. Gierens, E. Girard, V. Giruad, C. Jakob, E. A. Jensen, V. Khvorostyanov, M. Koehler, A. Lare, R. Li, K. Maruyama, M. Montero, W. Tao, Y. Wang, D. Wilson, 2000, “Comparison of cirrus cloud models: A project of the GEWEX cloud systems study (GCSS) working group on cirrus cloud systems”, 13th International conference on clouds and precipitation, 1, 1-4, Reno. (2) Q. Fu and K. N. Liou, 1992, “On the correlated k-distribution method for radiative transfer in non-homogeneous atmospheres”, J. Atmos. Sci., 49, 2139-2156. (3) Q.Fu and K. N. Liou, 1993, “Paramterization of the radiative properties of cirrus clouds”, J. Atmos. Sci. 50, 2008-2025. (4) Q. Fu, 1996, “An accurate parameterization of the solar radiative properties of cirrus clouds for climate models”, J. Climate, 9, 2058-2082. (5) Q. Fu, P. Yang and W. B. Sun, 1998,“An accurate parameterization of the infrared properties of cirrus clouds for climate models”, J. Climate, 11, 2223-2237. (6) R. G. Pinnick, S. G. Jennings, P. Chylek, C. Ham, W. T. Grandy, 1983, “Backscatter and extinction in water clouds”, J. Geophys. Res., 88, 6787-6796. (7) R. J. Cotton and P. Brown, 2004, “Ice initiation and evolution in large-eddy simulations using prognostic ice nuclei and CCN”, 11th Conference on Cloud Physics, P2, 16. For more information about this poster please contact Dr John Marsham, Environment, School of Earth and Environment, The University of Leeds, Leeds, LS2 9JT Email: jmarsham@env.leeds.ac.uk Tel: +44 (0)113 3437531