Geoscience Reference
In-Depth Information
Tabl e 3. 1 Cloud microphysical parameters used in the MCMC-based parameter sensitivity
experiments, along with truth values for the simulated observation experiment and parameter
ranges. Note that all values are reported in CGS units to be consistent with what is used in the
model formulation and inverse method
Parameter description
Abbreviation
Units
Truth
Min
Max
cm 1 b s
Snow fall speed coefficient
a s
200:0
50:0
1;000:0
Snow fall speed exponent
b s
None
0:3
0:1
1:0
a g
cm 1 b g
400:0
50:0
1;200:0
Graupel fall speed coefficient
Graupel fall speed exponent
b g
None
0:4
0:1
0:9
N 0r
cm 4
0:5
0:0
5:0
Slope intercept of the
rain particle size distribution
Slope intercept of the
N 0s
cm 4
0:5
0:0
5:0
snow particle size distribution
Slope intercept of the
cm 4
N 0g
0:5
0:0
5:0
graupel particle size distribution
Snow particle density
cm 3
s
g
0:2
0:1
1:0
cm 3
Graupel particle density
g
g
0:4
0:1
1:0
kg 1
Threshold cloud mass mixing ratio
q c 0
g
1:0
0:1
3:0
for autoconversion to rain
Fig. 3.6 Simulated 10-cm
wavelength radar reflectivity
(dBZ) for the 1D emulated
squall line (Adapted from
van Lier-Walqui et al. ( 2012 ),
Fig. 1)
Simulated Radar Reflectivity (dBZ)
15000
40
35
30
10000
25
20
15
5000
10
5
0
0
20
40
60
80
100
120
140
160
180
Time (min)
stratiform (Fig. 3.6 -simulated radar reflectivity from van Lier-Walqui et al. 2012 ).
Note that though there is no bright-band simulator in the radar forward model, the
effects of melting snow and graupel are accounted for in the model. For additional
details on the model configuration, the reader is referred to Posselt and Vukicevic
( 2010 ), Posselt and Bishop ( 2012 ), and van Lier-Walqui et al. ( 2012 ).
A MCMC algorithm very similar in form to that implemented for the afore-
mentioned ice cloud property retrieval is used to examine how changes in each of
ten cloud microphysical parameters affect output precipitation, liquid and ice water
path, and radiative fluxes (Table 3.2 ) for the idealized deep convective squall line.
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