合并大量netCDF文件

我有一个包含 netCDF (.nc) 文件的大文件夹，每个文件都具有相似的名称。数据文件包含时间、经度、纬度和月降水量的变量。目标是获取 X 年内每个月的平均月降水量。所以最后我会得到 12 个值，代表 X 年内每个纬度和经度的平均月降水量。多年来，每个文件都位于同一位置。 每个文件都以相同的名称开头并以“date.sub.nc”结尾，例如：

'data1.somthing.somthing1.avg_2d_Ind_Nx.200109.SUB.nc'
'data1.somthing.somthing1.avg_2d_Ind_Nx.200509.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201104.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201004.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201003.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201103.SUB.nc'
'data1.somthing.somthing1.avg_2d_Ind_Nx.201203.SUB.nc'

结局是 YearMonth.SUB.nc 到目前为止我所拥有的是：

array=[]
f = nc.MFDataset('data*.nc')
precp = f.variables['prectot']
time = f.variables['time']
array = f.variables['time','longitude','latitude','prectot'] 

我收到一个 KeyError：（'时间'，'经度'，'纬度'，'prectot'）。有没有办法结合所有这些数据，以便我能够操纵它？

正如@CharlieZender 提到的，ncra是这里的方法，我将提供有关将该函数集成到 Python 脚本中的更多详细信息。 （PS - 您可以使用 Homebrew 轻松安装 NCO，例如http://alejandrosoto.net/blog/2014/01/22/setting-up-my-mac-for-scientific-research/ http://alejandrosoto.net/blog/2014/01/22/setting-up-my-mac-for-scientific-research/)

import subprocess
import netCDF4
import glob
import numpy as np

for month in range(1,13):
    # Gather all the files for this month
    month_files = glob.glob('/path/to/files/*{0:0>2d}.SUB.nc'.format(month))


    # Using NCO functions ---------------
    avg_file = './precip_avg_{0:0>2d}.nc'.format(month)

    # Concatenate the files using ncrcat
    subprocess.call(['ncrcat'] + month_files + ['-O', avg_file])

    # Take the time (record) average using ncra 
    subprocess.call(['ncra', avg_file, '-O', avg_file])

    # Read in the monthly precip climatology file and do whatever now
    ncfile = netCDF4.Dataset(avg_file, 'r')
    pr = ncfile.variables['prectot'][:,:,:]
    ....

    # Using only Python -------------
    # Initialize an array to store monthly-mean precip for all years
    # let's presume we know the lat and lon dimensions (nlat, nlon)
    nyears = len(month_files)
    pr_arr = np.zeros([nyears,nlat,nlon], dtype='f4')

    # Populate pr_arr with each file's monthly-mean precip
    for idx, filename in enumerate(month_files):
        ncfile = netCDF4.Dataset(filename, 'r')
        pr = ncfile.variable['prectot'][:,:,:]  
        pr_arr[idx,:,:] = np.mean(pr, axis=0)
        ncfile.close()

    # Take the average along all years for a monthly climatology
    pr_clim = np.mean(pr_arr, axis=0)  # 2D now [lat,lon]