Laserfiche WebLink
<br />DECEMBER 19~ 1 <br /> <br />SILVERMAN, ROGERS AND DAHL <br /> <br />1469 <br /> <br />point measurements of precipitation but are theo- <br />retically generated by a continuous spatial distri- <br />bution function so that the true precipitation char- <br />acteristics are known quantities. <br /> <br />2. Approach <br /> <br />Storm characteristics such as type, structure, du- <br />ration, and speed and direction of movement affect <br />the size, shape, and precipitation gradient of the <br />isohyetal pattern it produces. The distribution of sur- <br />face rainfall will vary greatly between storms of sim- <br />ilar area and duration, particularly for convective <br />storms in which cells d,evelop and coexist at various <br />times and locations during the storm's lifetime <br />(Crane, 1979). The isohyetal pattern of moving con- <br />vective storms, therefore, will frequently contain <br />multiple peaks and will rarely be regular iri shape. <br />An example of a convective storm isohyetal pattern <br />is shown in Fig. 1. <br />The complexity and variability of convective storm <br />isohyetal patterns make it difficult to model the en- <br />tire storm pattern in a general manner. However, we <br />can consider that the isohyetal pattern of the storm <br />is composed of smaller isolated precipitation areas <br />which are characterized by closed isohyets and model <br />these components individually. Therefore, we adapt <br />the methodology of Schickedanz (1974) and, for the <br />purpose of this analysis, use the surface raincell <br />which he states is the smallest definable rain-pro- <br />ducing entity of a storm and shows that it generally <br />reflects conditions within the convective cells aloft. <br />Schickedanz (1974) defined a raincell in the follow- <br />ing manner: "A raincell in a multicellular system is <br />a closed isohyetal entity within the overall enveloping <br />isohyet of a rain-producing system; that is, it de- <br />scribes an isolated area of significantly greater in- <br />tensity than the system-enveloping isohyetal. When <br />raincells develop apart from a multicellular storm <br />system, the system-enveloping isohyet will not be <br />present, and the single cell is uniquely defined by the <br />separation between rain and no rain." He defined a <br />storm as an entity of rain, one or more raincells and/ <br />or areas of rain, that can be identified with a specific <br />synoptic weather classification and is separated from <br />other entities by 32 km and/or 1 h between rain end <br />and start times. According to these definitions, it can <br />be seen from Fig. 1 that the illustrated storm is com- <br />posed of three raincells. <br />The present analysis of the sampling variance of <br />raingage networks will be based on the raincell. The <br />gage density that is required to quantitatively resolve <br />the smallest size raincell that contributes meaning- <br />fully to the total storm rainfall also determines the <br />upper bound of gage density (densest) that is re- <br />quired to resolve the entire storm with at least the <br />same accuracy. <br />Local random variability may also contribute to <br /> <br /> <br />I. <br /> <br /> <br /> <br />FIG, 1. An example of an isohyetal pattern of total rainfall for <br />a Montana convective storm illustrating its raincell structure, <br />Isohyets are in mm, <br /> <br />the sampling variance of raingage networks. Storm <br />and raincell isohyets are generally drawn fairly <br />smoothly by precipitation data analysts. However, <br />if isohyets were drawn to rigorously fit the point rain- <br />fall data, they would be quite irregular. How much <br />of this irregularity is due to measurement-related <br />errors (e.g., evaporation, gage inclination, splash, <br />reading, exposure and wind effects) and how much <br />is due to local, storm-related random variability is <br />not known. <br />Of all the measurement-related errors, exposure <br />and wind have the greatest effect on gage catch, the <br />others being estimated by Kurtyka (1953)2 to be an <br />average of 1.5%. Larson and Peck (1974) have shown <br />that the effect of wind speed on gage catch increases <br />with increasing wind speed. The average gage catch <br />deficiency for rain is 12% at 4 m S-I, increasing to <br />18% at 8 m S-I. These errors can be reduced, but not <br />eliminated, by the use of gage shields and proper site <br />selection. Woodley et al. (1975) examined the ac- <br /> <br />2 Kurtyka, J, C" 1953: Precipitation measurements study. Rep, <br />of Investigation No, 20, Illinois State Water Survey Division, 178 <br />pp. <br />