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<br />SEPTEMBER 1981 D A V I D A. MAT THE W S
<br />iJ' ' ,
<br />~as analyzed to determine frequency of occurrence TABLE 1. Rawinsonde sllmple sizes.
<br />when several soundings were observed on one day
<br />(Tables 2, 7, 8 and 9).
<br />
<br />b. Satellite observations
<br />
<br />Geosynchronous satellite images at local noon
<br />(1800 GMT) and late afternoon (2300 GMT) were
<br />collected in 1976 and 1977. These data provided 2 km
<br />resolution visible images of the HIPLEX field sites.
<br />This imagery was received from the National
<br />Environmental Satellite Service, Satellite Field Ser-
<br />vice Station in Kansas City, Missouri, continuously
<br />during the HIPLEX field season. Daily analyses of
<br />the imagery were performed for the region within
<br />250 km of each field site.3 These analyses deter-
<br />mined the dominant type of convective cloud within
<br />250 km of the site and the typical cloud within 75 km
<br />of the site if this differed significantly from that
<br />within 250 km. Mesoscale cloud characteristics
<br />within 250 km of the site were also described to dis-
<br />tinguish between organized or mesoscale-triggered
<br />convection and isolated "air mass" convective
<br />cloud development. These satellite observations
<br />were u'sed to stratify cloud-model thermodynamic
<br />analyses in 1976 and 1977 and are discussed in
<br />Section 5.
<br />
<br />3. The model-sensitivity and qualifications
<br />
<br />a. Model application
<br />
<br />One-dimensional steady-state parcel models pro-
<br />vide an objective analysis of, basic thermodynamic
<br />features which affect the potential for convective
<br />cloud growth. They quantify the manual analysis of
<br />positive area above the convective condensation
<br />level (CCL) found on a thermodynamic diagram.
<br />This highly simplified, fast, numerical simulation of
<br />parcel buoyancy provides a systematic measure-
<br />ment tool to determine the relative potential for
<br />convective cloud growth and its intensity. These
<br />characteristics of convection are diagnosed by the
<br />model in real time using the rawinsonde data for
<br />model initialization. These data are analyzed in
<br />terms of cloud properties such as cloud-base height,
<br />cloud-top height, maximum vertical velocity of the
<br />parcel and other quantitative properties which may
<br />be compared for different soundings and locations
<br />to describe the natural variability of potential for
<br />convective cloud growth. Due to the simple and in-
<br />complete microphysics and dynamics of these
<br />models, care must be taken in the interpretation of
<br />results. Meaningful comparisons between model-
<br />
<br />3 Matthews, D. A., 1980: Summary of clouds and mesoscale
<br />forcing observed from synchronous meteorological satellites
<br />during the High Plains Cooperative Program,1976-1977. Tech-
<br />nical Report, Office of Atmospheric Resources Research, Water
<br />and Power Resources Service, Denver, 275 pp.
<br />
<br />
<br />Location 1975 1976 1977
<br />Miles City, Montana (MLS)* 251 160 148
<br />Billings, Montana (BIL) 91
<br />Baker, Montana (BA) 78
<br />Glendiv,~, Montana (GD)* 231
<br />Plevna, Montana (PL)* 232
<br />Goodland, Kansas (GLD) 47 158 202
<br />Limon, Colorado (LIC) 122
<br />South Park, Colorado (SPK) 112
<br />Big Spring, Texas (BGS) 57 157 162
<br />Midland, Texas (MAF) 193 229
<br />Total 1011 782 837
<br />* 1975 mesonet stations.
<br />
<br />Total
<br />sample
<br />
<br />559
<br />91
<br />78
<br />231
<br />232
<br />407
<br />122
<br />112
<br />376
<br />422
<br />
<br />2630
<br />
<br />diagnosed cloud properties and those of a specific
<br />observed cloud are difficult, it not impossible, due
<br />to the complex three-dimensional time-dependent
<br />nature of cloud growth. Nevertheless, useful
<br />thermodynamic characteristics of individual clouds
<br />within a field of clouds may be obtained from model
<br />analyses which quantify the potential for convection
<br />in a given area and time. Such results may be inter-
<br />preted as general thermodynamic characteristics
<br />which may be useful for examining the natural
<br />variability of convective potential. In this paper
<br />model results are studied' from this broader context.
<br />The atmosphere's capacity to support convective
<br />cloud development, as defined by the cloud depth
<br />diagnosed by the model, is probably the most use-
<br />ful information available from one-dimensional,
<br />steady-state cloud models ,(Silverman, 1976). This
<br />"dynamic potential" for convective cloud growth
<br />is directly related to the positive area observed on a
<br />thermodynamic diagram. Due to the limited ability
<br />of one-dimensional steady-state models to cor-
<br />rectly diagnose precipitation (Sax, 1972; Weinstein,
<br />1972b), this study focuses on the model's strength
<br />in diagnosis of the dynamic potential for convective
<br />cloud growth (Sax, 1969; Marwitz et al., 1970;
<br />Simpson and Dennis, 1974).
<br />
<br />b. Model characteristics
<br />
<br />GPCM is a one-dimensional steady-state
<br />Lagrangian parcel model similar to the Weinstein-
<br />Davis model (1968) and modified by Hirsch (1971)
<br />to include parmeterizations of Berry (1967), Kessler
<br />(1%9) and Wisner et al. (1972) for microphysical
<br />processes of cloud water, rainwater and cloud ice
<br />and graupe1 formation. The model predicts a spec-
<br />trum of clouds, depending upon the initial up-
<br />draft radius. Caution must be used in interpreta-
<br />tion of model results due to the sensitivity of the
<br />model to "updraft radius" and cloud-base height.
<br />These properties must be carefully matched with
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