Conversion to time series format

For a lot of applications it is favorable to convert the image based format into a format which is optimized for fast time series retrieval. This is what we often need for e.g. validation studies. This can be done by stacking the images into a netCDF file and choosing the correct chunk sizes or a lot of other methods. We have chosen to do it in the following way:

  • Store only the reduced gaußian grid points since that saves space.
  • Further reduction the amount of stored data by saving only land points if selected.
  • Store the time series in netCDF4 in the Climate and Forecast convention Orthogonal multidimensional array representation
  • Store the time series in 5x5 degree cells. This means there will be 2566 cell files (without reduction to land points) and a file called which contains the information about which grid point is stored in which file. This allows us to read a whole 5x5 degree area into memory and iterate over the time series quickly.

This conversion can be performed using the merra_repurpose command line program. An example would be:

merra_repurpose /merra2_data /timeseries/data -s 2000-01-01 -e 2018-11-30 --parameters SFMC RZMC --temporal_sampling 6

Which would take MERRA-2 data stored in /merra2_data from January 1st 2000 to November 30th 2018 and store the parameters for 6-hourly sampled surface (SFMC) and root zone soil moisture (RZMC) as time series in the folder /timeseries/data.

Conversion to time series is performed by the repurpose package in the background. For custom settings or other options see the repurpose documentation and the code in merra.reshuffle.

Note: If a RuntimeError: NetCDF: Bad chunk sizes. appears during reshuffling, consider downgrading the netcdf4 library via:

conda install -c conda-forge netcdf4=1.2.2

if you are on Python 2.* and

conda install -c conda-forge netcdf4=1.2.8

if you are using Python 3.*.

Reading converted time series data

For reading the data the merra_repurpose command produces the class MerraTs:

from merra.interface import MerraTs

# specify path to data folder
path = '../timeseries/data'

# specify location lon and lat
lon, lat = (16.375, 48.125)

# initialize the time series class
merra_reader = MerraTs(ts_path=path,

# read SFMC time series at the location
ts =, lat)