Graduate Thesis Or Dissertation
 

Evaluation of a probabilistic quantitative precipitation forecasting experiment

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/td96k553d

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  • Forecasts of the likelihood of occurrence of various amounts of precipitation are very important, since excessive precipitation amounts over relatively short time periods can have adverse effects on public safety and economic efficiency, As a result, forecasters at the National Weather Service Forecast Office in San Antonio, Texas were asked to formulate subjective probabilistic quantitative precipitation forecasts on an experimental basis beginning in February 1981. This study describes methods of evaluating probability forecasts of this ordinal variable and presents some results of the first year of the experiment. Scalar and vector evaluation procedures are described. In the case of scalar evaluation, the inclusion of a no-skill line and a no-correlation line on reliability diagrams is helpful in representing the skill, reliability, and resolution qeometrically in two-state situations. Geometrical interpretations of attributes of forecasts can also be accomplished in three-state situations based on vector evaluation procedures. A skill score for subsample forecasts is shown to be useful in identifying systematic errors made by forecasters or forecast systems. A beta model is developed to obtain a forecaster's predictive distributions (i.e., the distribution of use of probability values). The experimental results show that the skill of the subjective forecasts is generally higher than the skill of objective guidance forecasts for measurable precipitation (i.e., precipitation amounts exceeding a threshold of 0.01 inches), but that the opposite is true for threshold associated with larger precipitation amounts. This result is due primarily to the forecaster's tendency to over forecast for the events associated with higher precipitation thresholds. The tendency to over forecast is most pronounced in the nighttime forecasts and in the forecasts for drier stations. The MCS objective guidance forecasts, on the other hand, are quite reliable for both periods and all stations. The vector evaluation approach indicates that the degree of overforecasting is quite high for bimodal forecasts and that the skill contribution from bimodal forecasts is negative in many cases.
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