两类天气预报评分问题研究及一种新评分方法

探讨了预报评价的意义及应遵循的原则, 对常用的几种两类预报评分方法进行分析, 指出其应用的局限性, 得到一个判定所作预报水平是否高于随机预报、具有预报技巧的简易判别式; 提出评分权重的概念, 指出以往评分存在问题的根源是评分权重分配不当, 使评分结果的真实性受到影响, 评分无可比性, 进而提出一种考虑了评分权重的新评分方法。新评分方法满足预报评价的原则, 侧重于对两类事件中事件概率较小一方预报效果的评估, 评分结果不受事件概率影响, 具有可比性。对比分析表明:新方法比其他方法优越, 能更准确地反映预报水平, 使不同季节、不同地域的预报评分可进行比较, 是一个通用的评分方法。

Dichotomous Weather Forecasts Score Research and a New Measure of Score

The significance of forecast estim ate and the principles are discussed. It is assumed that the scores are objective; and moreover they can objectively reflect the forecast level.The scores should be comparable, guiding forecast in the right direction. Several usual methods of dichotomous forecasts score are analyzed, revealing that accuracy and critical success index (CSI) in different areas are incomparable due to the influence of event possibility. It shows that the true skill statistic (TSS) is approach to the probability of detection (POD) when forecasting rare events.When events do not appear but false alarms are made, TSS can't be calculated. Heidke skill score and Girbet skill score make up for the above weaknesses. The three skill scores are all obtained by comparing forecast with random ones, hence there are (n₁₁n₂₂-n₁₂n₂₁) in the three formulas. It can be used as the discriminant for forecast skill. When (n₁₁n₂₂-n₁₂n₂₁)> 0, it indicates that the forecast level is better than that of random forecast, otherwise, it will be worse than it. On the basis of the relationship between event probability and the difficulty of forecast, a new method of score weight is considered and proposed. The essay points out the exiting problems are resulted from improper score weight, leading the score result unreliable and not comparable. The new method of score is based on CSI, and combines CSI of the two event forecasts. The focus of score is laid upon estimating the one with smaller probability in the two events.The principles of forecast score are fulfilled. By comparative analysis, the new method is proved to be superior to other methods, especially on estimating rare events. They can reflect the forecast level and changes more accurately.The advantages are as follows :With the increase of samples, the new score tends to be more stable than other scores in the rare events fo recast, thus leading to a rapid judgement for forecast level. When forecast level is improved, the new score will be able to reflect it correctly and distinctly. The new score is objective, just and real, and is compatible for different seasons and regions. So it is a uniform standard in forecast score.