Combine Multiple Netcdf Files Into Timeseries Multidimensional Array Python

I am using data from multiple netcdf files (in a folder on my computer). Each file holds data for the entire USA, for a time period of 5 years. Locations are referenced based on th

Solution 1:

Nice start, I would recommend the following to help solve your issues.

First, check out ncrcat to quickly concatenate your individual netCDF files into a single file. I highly recommend downloading NCO for netCDF manipulations, especially in this instance where it will ease your Python coding later on.

Let's say the files are named precip_1.nc, precip_2.nc, precip_3.nc, and precip_4.nc. You could concatenate them along the record dimension to form a new precip_all.nc with a record dimension of length 58400 with

ncrcat precip_1.nc precip_2.nc precip_3.nc precip_4.nc -O precip_all.nc

In Python we now just need to read in that new single file and then extract and store the time series for the desired grid cells. Something like this:

import netCDF4
import numpy as np

yindexlist = [1,2,3]
xindexlist = [4,5,6]
ngridcell = len(xidx)
ntimestep = 58400# Define an empty 2D array to store time series of precip for a set of grid cells
timeseries_per_grid_cell = np.zeros([ngridcell, ntimestep])

ncfile = netCDF4.Dataset('path/to/file/precip_all.nc', 'r')

# Note that precip is 3D, so need to read in all dimensions
precip = ncfile.variables['precip'][:,:,:]

for i inrange(ngridcell):
     timeseries_per_grid_cell[i,:] = precip[:, yindexlist[i], xindexlist[i]]

ncfile.close()

If you have to use Python only, you'll need to keep track of the chunks of time indices that the individual files form to make the full time series. 58400/4 = 14600 time steps per file. So you'll have another loop to read in each individual file and store the corresponding slice of times, i.e. the first file will populate 0-14599, the second 14600-29199, etc.

Solution 2:

You can easily merge multiple netCDF files into one using netCDF4 package in Python. See example below:

I have four netCDF files like 1.nc, 2.nc, 3.nc, 4.nc. Using command below all four files will be merge into one dataset.

import netCDF4
from netCDF4 importDatasetdataset= netCDF4.MFDataset(['1.nc','2.nc','3.nc','4.nc'])

Solution 3:

In parallel to the answer of N1B4, you can also concatenate 4 files along their time dimension using CDO from the command line

cdo mergetime precip1.nc precip2.nc precip3.nc precip4.nc merged_file.nc 

or with wildcards

cdo mergetime precip?.nc merged_file.nc 

and then proceed to read it in as per that answer.

You can add another step from the command line to extract the location of choice by using

cdo remapnn,lon=X/lat=Y merged_file.nc my_location.nc

this picks out the gridcell nearest to your specified lon/lat (X,Y) coordinate, or you can use bilinear interpolation if you prefer:

cdo remapbil,lon=X/lat=Y merged_file.nc my_location.nc