A python script using the iris library (http://scitools.org.uk/iris/) to compute time-series of the area-weighted mean value of a supplied netCDF data set writing the data to a new file

arctic_mean.py

#Crown Copyright, 2016

import iris, sys

def dict_diff(dict_a, dict_b):
    ''' return a dictionary showing differences between two dictionaries'''
    result = {}
    for key in set(dict_a.keys()+dict_b.keys()):
        if ((key in dict_a and (not key in dict_b or dict_a[key] != dict_b[key])) or
            (key in dict_b and (not key in dict_a or dict_a[key] != dict_b[key]))   ):
           result[key] = (dict_a.get(key, None), dict_b.get(key, None))
    return result

def synchronise_attributes(cubelist):
    ''' given an iris cubelist overwrite any attributes (metadata) which would prevent
    the concatenation into a single object'''

    attributes_to_overwrite = set()
    if len(cubelist) == 1:
	return cubelist

    c0 = cubelist[0]
    for c in cubelist[1:]:
        attribute_differences = dict_diff(c0.attributes,c.attributes).keys()
        attributes_to_overwrite.update(set(attribute_differences))

    for att in attributes_to_overwrite:
        for c in cubelist:
            c.attributes[att] = "overwritten"


#deal with arguments in simple way
if len(sys.argv) != 3:
   print "usage: ", sys.argv[0], "[source directory] [destination directory]"
   print "data from within the Arctic circle is read, spatially averaged and written to the destination file"
   sys.exit()

data_dir = sys.argv[1]
output_dir = sys.argv[2]

# More complicated arguments can/should be dealt with using the argparse module

# load all files from the source directory, modifiying metadata as they are loaded
var_cubelist = iris.load(data_dir + "/*.nc")

# To construct a single time series the metadata in the different variables (iris cubes) needs to be 
# synchronised. If the metadata is already consistent then nothing is done.

synchronise_attributes(var_cubelist)

# with consistent metadata a single iris cube should be obtained.
var_cube = var_cubelist.concatenate_cube()


#extract all data north of 67 N
#construct Constraint object
latitude_constraint = iris.Constraint(latitude= lambda lat: lat > 67.)

# use the latitude constraint to subset the data being processed
var_arctic = var_cube.extract(latitude_constraint)

#calculate weights for area averaging
aw = iris.analysis.cartography.area_weights(var_arctic)

#calculate area weighted mean over whole arctic region
var_arctic_ts = var_arctic.collapsed(['latitude','longitude'], iris.analysis.MEAN, weights = aw)

#write results to the specified file
# construct netCDF file name
output_file = output_dir +"/arctic" + data_dir.replace("/","_")+".nc"
# write to disk
iris.save(var_arctic_ts, output_file, unlimited_dimensions = ['time'])