to the gauge measured daily or for an event, since studies show that gauge catch varies by individual precipitation event.” Nice for an overview Could be done for daily, hourly (operational) and 30min-events (and other time-intervals) to get a feeling for differences caused by different time scales Catch ratio versus wind speed – regression analysis per gauge (Goodison et al., 1998, chapter 4.4.2 (example Tretyakov)) “Regression analysis of all daily gauge catch ratios as a function of the daily wind speed at gauge height and daily air temperature gave best-fit regression equations for the different types of precipitation. […] A similar analysis of data for individual sites […] could result in different equations. It is critical, however, that a representative range of wind speed be sampled so that any derived equation can be applied to a range of sites with differing wind speeds. Goodison, et al., 1998, chapter 4.8 (summary): “Regression analysis was used to develop relationships of catch ratio versus wind and temperature. Multiple linear regression (MLR) was applied for the initial screening of significant variables. [..] Using the MLR results as a guide, non- linear regression analysis was applied to obtain improved fits where appropriate. In some cases, using a second order polynomial or exponential form of the wind variable improved the regression coefficient. The final regression equations […] are given in Table 4.8.1: Discussion (Goodison et al., 1998, chapter 4.9): Applying a DFIR > 3 mm minimum threshold to data sets This threshold was set in the protocol for analyzing the data, in order to minimize the large variations in catch ratio caused by small absolute differences between the gauges. By applying this threshold,
to the gauge measured daily or for an event, since studies show that gauge catch varies by individual precipitation event.” Nice for an overview Could be done for daily, hourly (operational) and 30min-events (and other time-intervals) to get a feeling for differences caused by different time scales Catch ratio versus wind speed – regression analysis per gauge (Goodison et al., 1998, chapter 4.4.2 (example Tretyakov)) “Regression analysis of all daily gauge catch ratios as a function of the daily wind speed at gauge height and daily air temperature gave best-fit regression equations for the different types of precipitation. […] A similar analysis of data for individual sites […] could result in different equations. It is critical, however, that a representative range of wind speed be sampled so that any derived equation can be applied to a range of sites with differing wind speeds. Goodison, et al., 1998, chapter 4.8 (summary): “Regression analysis was used to develop relationships of catch ratio versus wind and temperature. Multiple linear regression (MLR) was applied for the initial screening of significant variables. [..] Using the MLR results as a guide, non- linear regression analysis was applied to obtain improved fits where appropriate. In some cases, using a second order polynomial or exponential form of the wind variable improved the regression coefficient. The final regression equations […] are given in Table 4.8.1: Discussion (Goodison et al., 1998, chapter 4.9): Applying a DFIR > 3 mm minimum threshold to data sets This threshold was set in the protocol for analyzing the data, in order to minimize the large variations in catch ratio caused by small absolute differences between the gauges. By applying this threshold,
5-2(5)_Analysis_Methods_v4.docx