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I'm trying to calculate the monthly modes of variability with some daily sea level pressure data. It shows the error when checking the time axis:
check_monthly_time_axis
raise ValueError(
ValueError: DATA ERROR: time is not correct!
I guess the error is because daily data has more time steps than the desired monthly data?
Describe the solution you'd like
It would be convenient to automatically convert daily (or even sub-daily? I don't know if anyone would use such simulations) into monthly data (or seasonal/annual, as we have the option to calculate these temporal frequencies in the PMP config file).
Describe alternatives you've considered
Or is it encouraged to use monthly data because most climate models output PSL at monthly frequency?
Additional context
No response
The text was updated successfully, but these errors were encountered:
@ShihengDuan Hi, thank you for the feature suggestion. At the moment the modes of variability code expects monthly time series as its input. If your data is daily, you will need to convert it to monthly. But I do agree that it would be convenient to have that function. I will see what I can do.
During the mean time, I recommend using xcdat or xarray to convert daily to monthly mean dataset.
Is your feature request related to a problem?
I'm trying to calculate the monthly modes of variability with some daily sea level pressure data. It shows the error when checking the time axis:
I guess the error is because daily data has more time steps than the desired monthly data?
Describe the solution you'd like
It would be convenient to automatically convert daily (or even sub-daily? I don't know if anyone would use such simulations) into monthly data (or seasonal/annual, as we have the option to calculate these temporal frequencies in the PMP config file).
Describe alternatives you've considered
Or is it encouraged to use monthly data because most climate models output PSL at monthly frequency?
Additional context
No response
The text was updated successfully, but these errors were encountered: