免责声明:我不是一个气象学家。要回答这个问题,你需要了解一个预测。基本上,气象学家运行计算机模拟预测天气系统如何演变从当前测量。事实上,数据同化技术(http://en.wikipedia.org/wiki/Data_assimilation)是常用的,这样的预测模型不仅取决于当前的观测,但也对天气系统的历史。随着新数据进来,进化模型调整对这些值——但不是被迫完全匹配,会破坏历史信息。因此,任何一个模型是不完全准确。不过,如果我们同时运行很多模型,包括随机变化反映了天气系统的混沌性质(测量误差),我们得到一个叫做“集合预报”。这就是雨来自30%的几率:30%的模拟预测雨在你的区域,而其他人认为它将保持干燥。/最小/最大平均温度等也可以很容易地从集合中提取。因此,预测的准确性可以以不同的方式被认为是。 It is certainly possible to compare the predictions of one ensemble member to observations: this is an integral part of the data assimilation process. However, it is not necessarily a problem if a member performs poorly on one particular occasion. Different types of measurements may be weighted differently during the comparison (so matching rainfall might be deemed 'more important' than matching temperature; this may vary depending on the type of forecast/its intended use). More generally, you have to look at the long-term statistics of the errors in the forecast. So, if you look at several months of data, did it rain on around a third of the days when you predicted a 30% chance of rain? On average, are your temperature predictions reasonably accurate? Making this assessment on a day-by-day basis is unlikely to be in keeping with the statistical basis of modern weather forecasting.