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<br />missE!s is still 0 (critical density) were printed out. A typical result <br />showing the input and output data is shown in Fig. 2. <br /> <br />4. Results <br /> <br />A general examination of the effects of varying gage density (e.g., Fig. 2) <br />indicates that the sample mean precipitation amount, i, for all gage <br />densities used in this study is in excellent agreement with the true precipi- <br />tation amount, RTOTL. However, there is evidence that meaningful deviations <br />between i and RTOTL are beginning to appear in those cases for which <br />the precipitation gradients are very steep and for which the area of the <br />model raincell is very small cOl'llIpared to the gage density such that the <br />percentage of unsampled cases is high (>65 percent). The sample standard <br />deviation, s, increases rapidly with decreasing gage density in all cases. <br />The variability is caused primarily by: 1) the limitations in estimating <br />raincell area by summing grid subareas with nonzero gage precipitation and <br />2) the inability to determine and account for the precipitation gr,adient in <br />estimating the subarea precipitation amounts. The effects of both shortcomings <br />tend to be amplified as the gagl:! density decreases. <br /> <br />When the precipitation gradient is approximately linear, the fit of the <br />distribution of total precipitation estimates in a sample set to a normal <br />distribution is quite good. When the precipitation gradient is nonlinear, <br />the distribution is bimodal with about equal probability of over- and under- <br />estimating the true precipitation amount, depending on whether the particular <br />gage configuration locates a ga~II~(s) in the high-precipitation region of the <br />pattern or not, respectively. <br /> <br />10 <br />