Daily total rainfall from AUS2200 output

[Aidan (Aidan Heerdegen, ACCESS-NRI Release Team Lead)]

@cchambers

Could you tell me what this is calculating as I’m not too familiar with python, currently learning. Is this grabbing the 10 minute rainfall, summing it over the 6 times in each hourly file and then adding up to create a 24 hour total accumulated rain?

In this case I’m using xarray to read in all the data into a single virtual dataset (called ds for convenience), so the time-0 dimension has length 36, which was all the data that was available. If they’re 10 minutes apart then that is 6 hours, not 24.

So the python command below is accessing just the fld_s04i201 variable by accessing it directly as a key value for the ds object, then calling the .resample method to convert to daily (1D) time intervals and then calling the .sum() as the aggregating function for the resample operation

ds['fld_s04i201'].resample(time_0='1D').sum()

Here are some resources about the resample operation

Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen

Time series data

Efficient daily resampling using xarray

Good day. I often resample hourly netcdf data (eg. ERA5) into daily means with xarray. However, when doing this for large 4D dataset (eg. ERA5 file) it is very slow. Does anyone have a more efficient method that is effective and returns an xarray object? An example of code below: import xarray as xr tfile = ‘/g/data/rt52/era5/pressure-levels/reanalysis/t/2022/t_era5_oper_pl_20220301-20220331.nc’ ft=xr.open_dataset(tfile).resample(time=‘1D’).mean(keep_attrs=True)

Glad you got the cdo version working. The advantage of python is that it doesn’t have to create lots of intermediate data, which wastes space and can rapidly fill directories with lots of derivative data.

Also using python in jupyter notebooks provides a nicer data exploration interface, and provides some record of you work, which can be useful to refer to later on.