Hi
I am comparing two rainfall datasets at different time scales (e.g. daily, monthly and annual). I am using mean error, mean absolute error and root mean square error. One way to make comparison is using mm/day for daily, mean mm/day for monthly and annual scale. But I am using accumulated rainfall, as a result, the error statistics is not comparable at different scales (error increasing as moving from daily towards annual scale). Kindly suggest how to normalize my error statistics to make it comparable.
Thanks
Dear Asid,
You can convert the accumulated rainfall (e.g. mm/month) to mean daily rainfall (in mm/d). Then you will have rainfall amounts at different temporal scales in the same unit and hence the differences between the two datasets for different temporal resolutions can be compared.
If these two datasets are equally likely, then you should not call the differences between the datasets errors but rather differences.
Regards,
Martijn
A simple method can be to calculate relative error.
Relative error = Absolute error (what you calculated) / mean rainfall.
e.g.
Absolute error = 5 cm
Mean = 50 cm
Relative error = 5/50 *100 = 10%
Hope this helps.
Sasi
- Badges
- Science topic
- Similar topics
- Geoscience
- Atmospheric Sciences