合并大量netCDF文件

Question

我有一个很大的netCDF（.nc）文件文件夹，每个文件都有相似的名称。数据文件包含时间、经度、纬度和月降水量变量。目标是获得每个月X年的平均月降水量。因此，最终我会得到12个值，代表每个纬度和长纬度的X年平均月降水量。多年来，每个文件都位于同一位置。每个文件以相同的名称开头，以“date.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'

结尾是年月。目前为止我所知道的是:

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

我得到一个KeyError: ('time '，'经度'，'纬度'，' prectot ')。有没有一种方法可以把所有这些数据结合起来，让我能够操作它们？

Answer 1

ymonmean命令计算CDO日历月的平均值。因此，这项任务可以通过两条途径完成:

cdo mergetime data*.SUB.nc  merged.nc  # put files together into one series
cdo ymonmean merged.nc annual_cycle.nc # mean of all Jan,Feb etc. 

CDO还可以计算其他统计数据的年度周期，YMONSTD，YMONMAX等。时间单位可以是天或五角星以及月。（例如，Ydaymean）。

Answer 2

NCO通过以下方式完成此任务

ncra *.01.SUB.nc pcp_avg_01.nc
ncra *.02.SUB.nc pcp_avg_02.nc
...
ncra *.12.SUB.nc pcp_avg_12.nc
ncrcat pcp_avg_??.nc pcp_avg.nc

当然，前12个命令可以用Bash循环完成，将总行数减少到不到5行。如果您更喜欢使用python编写脚本，您可以使用此检查您的答案。ncra文档在这里。

Answer 3

正如@CharlieZender提到的，ncra是这里的方式，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 - 您可以使用自制软件轻松安装NCO，例如 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]