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read-tas-cru.py
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import numpy as np
from netCDF4 import Dataset
import pylab as plt
import os
import time
import datetime
from mpl_toolkits.basemap import Basemap
import netCDF4 as nc
import math
from urllib.request import urlretrieve
from scipy.interpolate import griddata
# set up Data directory
DataDir = "/Users/mingquan/projects"
# Set general information for the data source
remote_source = "https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.02/"
gist_source = "https://github.com/mmu2019/Datasets/blob/master/read-tas-cru.py"
local_source = DataDir + '/CRU/v4.02/cru_ts4.02.1901.2017.tmp.dat.nc'
stamp1 = '2019-05-06'
datestr = str(datetime.datetime.now())
TmpStr = datestr.split(' ')
stamp2 = TmpStr[0]
print(datestr)
print(stamp2)
instit = "Climatic Research Unit, School of Environmental Sciences, University of East Anglia, UK"
period = "1980-01 through 2017-12"
origtr = "monthly"
origsr = "0.5 degree"
origut = "C"
finltr = "monthly"
finlsr = "0.5 degree"
finlut = "K"
# Create temporal dimension
nyears = 38
nmonth = 12
smonth = np.asarray(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'])
month_bnd = np.asarray([0,31,59,90,120,151,181,212,243,273,304,334,365],dtype=float)
tbnd = np.asarray([((np.arange(nyears)*365)[:,np.newaxis]+month_bnd[:-1]).flatten(),
((np.arange(nyears)*365)[:,np.newaxis]+month_bnd[+1:]).flatten()]).T
tbnd += (1980-1850)*365
tbnd.shape
t = tbnd.mean(axis=1)
# Create spatial dimension
res = 0.5
latbnd = np.asarray([np.arange(- 90 , 90 ,res),
np.arange(- 90+res, 90+0.01,res)]).T
lonbnd = np.asarray([np.arange(-180 ,180 ,res),
np.arange(-180+res,180+0.01,res)]).T
lat = latbnd.mean(axis=1)
lon = lonbnd.mean(axis=1)
ntot = (2017-1900)*12
ntim = t.size
nlat = lat.size
nlon = lon.size
# read single netCDF file
filename = DataDir + '/CRU/v4.02/cru_ts4.02.1901.2017.tmp.dat.nc'
print(filename)
cru=Dataset(filename,'r',format='NETCDF3')
print(cru)
print(cru.variables)
tas = cru.variables['tmp']
long_name = tas.long_name
data = tas[ntot-ntim:ntot,:,:]
# convert unit from C to K
data[:,:,:] = data[:,:,:] + 273.14
print(t.shape)
print(tas.shape)
print(data.shape)
data_min = data.min()
data_max = data.max()
with Dataset(DataDir + "/tas.nc", mode="w") as dset:
# Create netCDF dimensions
dset.createDimension("time",size= t.size)
dset.createDimension("lat" ,size=lat.size)
dset.createDimension("lon" ,size=lon.size)
dset.createDimension("nb" ,size=2 )
# Create netCDF variables
T = dset.createVariable("time" ,t.dtype ,("time" ))
TB = dset.createVariable("time_bounds",t.dtype ,("time","nb"))
X = dset.createVariable("lat" ,lat.dtype ,("lat" ))
XB = dset.createVariable("lat_bounds" ,lat.dtype ,("lat","nb" ))
Y = dset.createVariable("lon" ,lon.dtype ,("lon" ))
YB = dset.createVariable("lon_bounds" ,lon.dtype ,("lon","nb" ))
D = dset.createVariable("tas" ,data.dtype,("time","lat","lon"), fill_value = -999.)
print(D.shape)
# Load data and encode attributes
# time
T [...] = t
T.units = "days since 1850-01-01"
T.calendar = "noleap"
T.bounds = "time_bounds"
TB[...] = tbnd
T.standard_name = "time"
T.long_name = "time"
# lat
X [...] = lat
X.units = "degrees_north"
XB[...] = latbnd
X.standard_name = "latitude"
X.long_name = "latitude"
# lon
Y [...] = lon
Y.units = "degrees_east"
YB[...] = lonbnd
Y.standard_name = "longitude"
Y.long_name = "longitude"
# data
D[...] = data
D.units = "K"
D.standard_name = "2-meter air temperature"
D.long_name = long_name
D.actual_range = np.asarray([data_min,data_max])
dset.title = "CRU time series (TS) high-resolution gridded datasets"
dset.version = "4.02"
dset.institutions = instit
dset.source = "monthly observations at meteorological stations across the world land areas"
dset.history = """
%s: downloaded source from %s;
%s: converted to netCDF with %s""" % (stamp1, remote_source, stamp2, gist_source)
dset.references = """
@ARTICLE{Harris2014,
author = {Harris, I., P.D. Jones, T.J. Osborn and D.H. Lister},
title = {Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset},
journal = {International Journal of Climatology},
year = {2014},
number = {34(3)},
page = {623-642},
doi = { https://doi.org/10.1002/joc.3711}
}"""
dset.comments = """
time_period: %s; original_temporal_resolution: %s; original_spatial_resolution: %s; original_units: %s; final_temporal_resolution: %s; final_spatial_resolution: %s; final_units: %s""" % (period, origtr, origsr, origut, finltr, finlsr, finlut)
dset.convention = "CF-1.7"