cloud resolving model studies of tropical deep convection observed during hibiscus 2004
DESCRIPTION
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; - PowerPoint PPT PresentationTRANSCRIPT
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.
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
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
24th February, 2004 Case Study• Large squall line moving from north passes
over Bauru.
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
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
Timeseries of max 3.5km radar reflectivity
Timeseries of 3.5km radar reflectivity modes
Log-Normal Distribution of 3.5km dBZ from 14:00-23:00
Timeseries of Max EchotopsM
axim
um o
f 10d
BZ
rad
ar e
cho
tops
(km
)
Local Time
Ma
xim
um
of
10
dB
Z r
ad
ar
ech
o t
op
s (k
m)
CCN = 720cm-3
CCN = 240cm-3
Radar data
Local Time
Mo
de
of
10
dB
Z r
ad
ar
ech
o t
op
s (k
m)
CCN = 720cm-3
CCN = 240cm-3
Radar data
Timeseries of Echotop Modes
Timeseries of Echotop Variances
Local Time
Va
ria
nce
of
10
dB
Z r
ad
ar
ech
o t
op
s (k
m2)
Log-Normal Distribution of Echotops from 14:00-23:00
-5
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
Log
10 o
f N
orm
alis
ed D
istr
ibut
ion
of E
cho
Top
s
Max Tracer at each Height as Percentage of Max Input
CFC-11 tracer measurements from SF-4 (DIRAC, UCAM)
Profile of Mean Liquid Water
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
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)