WMOD00582

• Title: Natural Variability of Thermodynamic Features Affecting Convetive Cloud Growth and Dynamic Seeding: A Comparative Summary of Three High Plain Sites from 1975 to 1977
• Date: 9/9/1981
• Weather Modification - Doc Type: Report

SEPTEMBER 1981

977

DAVID A. MATTHEWS

TABLE 4. Sample means for different cloud types observed in Miles City, Montana.in 1976-77 during days
for which satellite population statistics of convective clouds were available.

Variable

Group I mean
Cumulus

Group 2 mean
Cumulonimbus

P-value*

Sample size

n = 28

n = 9

Cloud-base temperature (oC)
Surface convective temperature
Mean lapse rate for layer 100-150 mb AGL roC (100 m)-I]
Mean lapse rate for layer 150-200 mb AGL roC (100 m)-I]
Mean mixing ratio for layer Sfc-50 mb AGL (g kg-I)
Mean mixing ratio for layer Sfc-100 mb AGL (g kg-I)
Natural cloud depth for 0.5 km initial radius (km)
Cloud-top temperature for 0.5 km initial radius (oC)
Cloud-top height for 0.5 km initial radius (km MSL)
Natural cloud depth for 1.0 km initial radius (km)
Cloud:top temperature for 1.0 km initial radius eC)
Total supercooled water content for 1.0 km initial radius (g kg-I)
Natural cloud depth** for 1.5 km initial radius (km)
Cloud-top. temperature** for 1.5 km initial radius eC)
, Total supercooled water content** for 1.5km initial radius (g kg-I)
Natural cloud depth for 10.0 km initial radius (km)
Modified cloud depth for 10.0 km initial radius (km)
Cloud-top temperature for 10.0 km initial radius (OC)
Total supercooled water content for 10.0 km initial radius (g kg-I)

6.96
8.36
-0.67
-0.60
9.07
8.59
1.54
-6.23
7.01
2.44
-12.62
1.32
3.01
-16.76
2.23
5.22
5.49
- 34.46
6.79

0.013
0.025
0.012
0.005
0.022
0.019
0:008
0.059
0.036
0.003
0.008
0.018
0.003
0.004
0.017
0.009
0.011
0.012
0.016

3.89
3.89
-0.81
-0.83
7.24
6.71
0.30
- 1.28
5.50
0.42
2.09
0.18
0.51
2.57
0.33
1.10
1.19
-7.39
1.10

Cloud type

Cumulus congestus
n = 12

Cumulonimbus
n = 28

Cloud-base height (km MSL)
Cloud-base temperature eC)
Surface'mixing ratio (g kg-I)
Mean mixing ratio for layer from Sfc to 50 mb AGL (g kg-I)
Mean mixing ratio for layer from Sfc to 100 mb AGL (g kg-')
Mean mixing ratio for layer from 100 to 150 mb AGL (g kg-')
Mean mixing ratio for layer from 150 to 200 mb AGL (g kg:-')
Total supercooled water content (g kg-I) for 1.0 km initial radius (g kg-I)
Total supercooled water content for 1.5 km initial radius (g kg-I)
Total supercooled water content for 2.0 km initial radius (g kg-')
Total supercooled water content for 3.0 km initial radius (g kg-I)
Total supercooled water content for 10.0 km initial radius (g kg-I)

3.74
2.42
8.22
7.33
6.37
5.69
4.81
0.08
0.32
0.48
0.75
1.65

3.01
6.96
9.76
9.07
8.59
7.42
6.36
1.32
2.23
3.06
4.11
6.79

0.013
0.001
0.025
0.003
0.002
0.000
0.000
0.007
0.009
0.009
0.012
0.017

* All variables whose P-values were <0.060 are listed in table 4.
** Similar variables were significant for the 2.0 km and 3.0 km initial radius cases.

maximum updraft speeds, and total supercooled
water (Table 4).
The P value in these cases is the probability that
the differences between variables of the two cloud
types could have occurred by chance. These results
show that cloud-model diagnoses are generally con-
sistent with the expected theoretical differences in
thermodynamic controls from one level of convec-
tive intensity to another; hence, we have confidence
in the value of these analyses in determining
general thermodynamic characteristics of use in
describing the natural variability of cloud growth.
Note that similar statistically significant results
(not shown) were obtained from the analyses of
cases from' GLD and BGS in 1976 and 1977 samples
when satellite observations of cloud types were
available (sample size of 157-202 soundings).

4. Natural variability of convective thermodynamics

a. Mesoscale spatial variability

1) DAILY V ARIABIUTY

The spatial variability of three simultaneous
mesoscale rawinsonde observations clearly showed
the need to verify individual model diagnoses of
thermodynamic potential for cloud growth. Analysis
of simultaneous multiple mesoscale rawinsonde
data observed in the 1975 Montana network (Fig.
1) shows large variations in mean lapse rates and
mean mixing ratios of six layers from the surface
to 50 kPa (Table 5). These variations in thermo-
dynamic variables resulted in significant differences
in model diagnosis of thermodynamic properties
of convective clouds on a given day and time. The