使用 python 平均多个 netCDF4 文件

Question

I am a bit of a netCDF in python noob so please excuse this noob question.

I have a folder filled with circa 3650 netCDF4 files. One file per day for a decade. the niles are named yyyymmdd.nc (eg 20100101,20100102,20100103,etc.). Each.nc file contains latitude, longitude, and temperature at one-time point for the same area - a section of the Tonga EEZ.

What I am trying to do is compute the average temperature for each lat and lon from across all files, ie I want to end up with one.nc file that has all the same lats and lons and average temperature across 10 years.

I have tried different things/versions of code, usually, they end up looking something like this.....

files = glob('*.nc')
ds = xr.open_mfdataset(files,)
mean = np.mean(ds['temp'][:, 0].values)

...... This code would give me the average temperature within a.nc file for all.nc files and not the average temperature based on lat and lon across a decade worth of files.

All and any help is much appreciated.

Thank you.

Answer 1

Assuming you are working on linux/macOS, this can be done easily using my nctoolkit package(see details here ).

The following will calculate the mean across all files and then plot the results:

import nctoolkit as nc
files = glob('*.nc')
ds = nc.open_data(file)
ds.ensemble_mean()
ds.plot()

nctoolkit uses CDO as a back-end by default, but can use NCO as well, which can result in a performance improvement. So the following might be faster:

import nctoolkit as nc
files = glob('*.nc')
ds = nc.open_data(file)
ds.ensemble_mean(nco=True)
ds.plot()

Answer 2

You can use the cdo package to do this using a wild card in the input file name. I've only tested it with a small number of files though, there is a caveat in that you might hit a system limit on the number of open files.

from cdo import *
cdo=Cdo()
cdo.ensmean(input='*.nc',output='ensmean.nc')

This is basically the equivalent of the command line call to cdo

cdo ensmean *.nc ensmean.nc 

That said, it sounds to me like it would be better to cat them together and then use timmean:

cdo.timmean(input=cdo.mergetime(input='*.nc'),output='timmean.nc')

which again is the python equivalent to

cdo mergetime *.nc all.nc
cdo timmean all.nc timmean.nc 

try both and see which one works/is fastest:-)