Transcript Slide 1

Large eddy model simulations of lidar and Doppler
radar data from a mixed phase cloud: constraining
vertical velocities and fallspeeds.
1Institute
John Marsham1, Steven Dobbie1 and Robin Hogan2
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 the importance 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.
Figure 1: Observed and simulated data. The LEM was
initialised with the 5 UTC Larkhill radiosonde (~20 km from
Observed
Simulated
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
IWC
IWC
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-tos(VD)
backscatter ratio of 18.5 sr for ice and water6.
s(VD)
• 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
VD
motions also appear to propagate against the mean-flow, so the
VD
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-top in reality and in the
Lidar
Lidar
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-tobackscatter ratio for ice may have been over-estimated.
Figure 2: There is two to three times more ice in the
Figure 5: The
LEM than observed. The LEM captures the increased
modelled bi-variate
turbulence at the cloud-top, which allows the LWC to
pdf of IWC and
form (this maximum is more sharply peaked in the LEM than
s(VD) is similar to
the observations, since the cloud-top is less variable in the
the pdf observed.
LEM). The LEM gives less turbulence within the cloud
Larger values of
and at cloud-base and so less LWC than observed at
s(VD)) are found for
cloud-base (although there is still a peak in s(VD) here).
smaller values of
IWC – at the edge
Figure 3: The LWP. The maximum LWP observed in a single column of the LEM is comparable with the
of convecting cells
microwave values, whilst the mean LWP in the LEM is much lower than observed – the LEM is producing
near the cloud-top.
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
Figure 6: Time averaged LEM
Figure 4: In this cloud
is little
mass-squared-weighted
s(VD) provides an
dependence of VD
terminal fallspeeds & radar
almost unbiased
or VZ on IWC. Ice
Doppler velocities. Modelled
estimate of s(w)
growing at the cloudmass-squared weighted
with a small random
top and sublimating
fallspeeds are 1.5 times
error.
at the cloud-base
larger than observed.
gives a bi-modal
distribution of VZ for
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
low IWC in the LEM.
retrieval. Radiosonde data were provided by BADC. This work was funded by the Natural Environment Research Council (NERC: NER/M/S/2002/00127).
= Radiosonde
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”,
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.
13th
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: [email protected] Tel: +44 (0)113 3437531