< p >让我们假设我有3个不同的气候模型为一个特定的区域项目的温度。我也有该地区的观察同样的时间框架(真正的温度)。每天我的阅读。我应该使用哪些指标,为什么?例如:< / p > < p >的意思是平均不是非常有用因为因为阅读可以相互抵消绝对意味着平均。我主要是认为这是最有用的。然而,我感到困惑,因为当我减去预测——观察我发现大约500个读数的13500有很大的不同(约摄氏10度)。我应该包括这些异常值或删除它们?方平均误差和根方意味着错误为我提供有价值的见解吗?编辑:嘿,每一个人。 Thank you for the feedback. My question at hand is that I am given 4 different bias correction methods and I want to create a multi model with equal voting. I want to present which bias correction method is more suitable: quantile mapping and scaled distribution mapping (Gamma and Normal Corrections). In order to do that(and to show that bias correction is useful) I found the Mean Absolute Error, Squared Mean Error and Root Squared Mean Error to find the most accurate model. Then performed bias correction with all methods and used the same metrics again. Also I created the multi model(basically it s the average of the models). Thus I created a plot with a single model, a multi model and a multi model with bias correction. With these metrics, I saw that the most accurate was the scaled distribution mapping with normal corrections which makes sense since temperature follows a normal distribution over the years (basically it has the lowest Mean Absolute Error) I based the comparison on MAE because I read that we usually use it to compare models etc Or should I just plot the outcomes of each bias correction method and see which one follows the distribution better?