小提前期预测系统的一致可靠性检验
Uniform reliability tests for forecasting systems with small lead time
摘要：
在评估预测系统的可靠性（或校准）时，一个长期存在的困难是，可靠性通常不是关于有限维参数的假设，而是关于整个函数关系。例如，二元事件的校准概率预测应该等于给定预测的{ \em any}值的事件的条件概率。试图在特定的预测值处估计与校准的偏差会遇到这样的困难，即假设该值的预测的概率通常为零。然而，考虑到估计的{ \em累积}与可靠性的偏差，提出了可以严格建立检验统计量的渐近分布的检验。分布结果是普遍的，提供的预测“看一步前”，或者换句话说，在未来的下一个时间步验证。此外，这些测试开发了针对广泛类别的替代品的能力。人工数据以及业务天气预报系统的数值实验也被提出，因为可能的扩展，以预测较长的前置时间。
英文摘要：
A long noted difficulty when assessing the reliability (or calibration) of forecasting systems is that reliability, in general, is a hypothesis not about a finite dimensional parameter but about an entire functional relationship. A calibrated probability forecast for binary events for instance should equal the conditional probability of the event given the forecast for {\em any} value of the forecast. Attempts to estimate deviations from calibration at a specific forecast value meet with the difficulty that the probability of the forecast assuming that value is typically zero. Considering the estimated {\em cumulative} deviations from reliability instead however, tests are presented for which the asymptotic distribution of the test statistic can be established rigorously. The distribution turns out to be universal, provided the forecasts "look one step ahead" only, or in other words, verify at the next time step in the future. Furthermore, the tests develop power against a wide class of alternatives. Numerical experiments for both artificial data as well as operational weather forecasting systems are also presented, as are possible extensions to forecasts with longer lead times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
