DOPPLER-RADAR ESTIMATES OF PRECIPITATION
i.e. a probability density, in increments of 0.25 standard deviation units, a spe- cific increment chosen to adequately visually divide the entire dataset evenly for an appropriately focused analysis. Applying this algorithm of normalizing the data, arranging it to present an inverse function, and binning the verti- cal axis in 0.25 standard deviation unit increments across the entire range of DREP produces the frequency distribu- tions defined by the normalized data analysis conducted for this study.
These results are now suitable to cas- cade into other computational models such as hydrologic models for floodplain assessment and dam reservoir assess- ment, among other topics. The results feed into a probabilistic distribution of likely values that cascades into other uses such as estimation of uncertain- ty in runoff predictions, uncertainty in soil-water contributions related to landslides, uncertainty in estimates of groundwater recharge from precipi- tation; among several other uses in Geoscience related investigations.
Conclusions
Figure 4 - Spectrum of the normalized Doppler Radar and Gauge Precipitation values with the probability density plot as it applies to each variable as the independent variable.
The assessment of uncertainty associ- ated with modern Doppler-Radar mea- surements of precipitation have several important sources of uncertainty. For example, variable Z-R relationships, radar calibration, clutter, attenuation, and an inaccurate understanding of the physics behind precipitation, along with instrumentation related factors, all contribute to uncertainty. Additionally, uncertainty exists in the operation of the Radar type as well as mathematical prediction applied to the collected data under investigation.
Figure 5 - Spectrum of the normalized Doppler Radar and Gauge Precipitation values with a kernel density function applied to one band of DREP (0 to 0.25).
24 TPG •
Jan.Feb.Mar 2019
Current research work attempts to display and quantify the uncertainty associated with the published data by use of typically normal statistical dis- tributions fitted to the data pairs of Doppler Radar estimated precipitation (DREP) versus precipitation gauge esti- mated precipitation (GEP). The analysis shows that the uncertainty in such data is significant, meaning such uncer- tainty indicates that a point estimate prediction is not appropriate, but this uncertainty can be well visualized using currently available data visualization computational software tools such as Microsoft Excel’s basic scatterplot tool. Further analysis using statistical pack- ages in R Studio or Python accomplish the next task: visualizing standard deviations of differences between the estimated DREP and GEP values.
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