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Dear all,
I found the temporal plot (Fig. 4) in Iturbe et al. (2018) (https://doi.org/10.1016/j.envsoft.2018.09.009) very useful.
I would like to get a quantitative measure of how similar different models are relative to EOBS. Is there any function in climate4R that allow to calculate this (spatial autocorrelation, etc)?
Regarding the temporal plot, instead of annual values, I would like to get the mean for each month for a 30-year period. However, if I use aggregateGrid(x, aggr.m= list(FUN = mean)), I get the mean for each month along the 30 year period. Is there any way of getting the overall monthly mean for the entire period (i.e. 30 year mean for January, 30 year mean for February, etc)?
Thanks in advance
Best regards,
José Pedro Ramião
The text was updated successfully, but these errors were encountered:
Hi,
For validation, I would recommend using the VALUE framework. It is implemented in climate4R through the package climate4R.VALUE.
Regarding the second question, you can use subsetGrid on a month-by-month basis and computing the monthly climatologies separately with function climatology. I hope this helps, sorry for our slow response.
Dear all,
I found the temporal plot (Fig. 4) in Iturbe et al. (2018) (https://doi.org/10.1016/j.envsoft.2018.09.009) very useful.
I would like to get a quantitative measure of how similar different models are relative to EOBS. Is there any function in climate4R that allow to calculate this (spatial autocorrelation, etc)?
Regarding the temporal plot, instead of annual values, I would like to get the mean for each month for a 30-year period. However, if I use aggregateGrid(x, aggr.m= list(FUN = mean)), I get the mean for each month along the 30 year period. Is there any way of getting the overall monthly mean for the entire period (i.e. 30 year mean for January, 30 year mean for February, etc)?
Thanks in advance
Best regards,
José Pedro Ramião
The text was updated successfully, but these errors were encountered: