def regrid(lat1,lon1,lats,lons,var,years):
"""
Interpolated on a 180x180 grid (latxlon) from CryoSat-2 using EASE2.0
100 km grid
"""
varn_re = np.reshape(var,(var.shape[0],(448*304)))
varn = np.empty((var.shape[0],lats.shape[0],lons.shape[1]))
print 'Completed: Start regridding process:'
for i in xrange(varn.shape[0]):
z = g((np.ravel(lat1),np.ravel(lon1)),varn_re[i,:],
(lats,lons),method='linear')
varn[i,:,:] = z
print 'Completed: Year %s Regridding---' % (years[i])
print 'Completed: Done regridding process!'
return varn
def regrid(lat1,lon1,lat2,lon2,var,years):
"""
Interpolated on selected grid. Reads PIOMAS in as 4d with
[year,month,lat,lon]
"""
varn_re = np.reshape(var,(var.shape[0],var.shape[1],(120*360)))
varn = np.empty((var.shape[0],var.shape[1],lat2.shape[0],lon2.shape[1]))
print 'Completed: Start regridding process:'
for i in xrange(varn.shape[0]):
for j in xrange(varn.shape[1]):
z = g((np.ravel(lat1),np.ravel(lon1)),varn_re[i,j,:],(lat2,lon2),method='linear')
varn[i,j,:,:] = z
print 'Completed: Year %s Regridding---' % (years[i])
return varn
def regrid(lat1, lon1, lats, lons, var, years):
"""
Interpolated on a 180x180 grid (latxlon) from CryoSat-2 using EASE2.0
100 km grid
"""
varn_re = np.reshape(var, (var.shape[0], (448 * 304)))
varn = np.empty((var.shape[0], lats.shape[0], lons.shape[1]))
print 'Completed: Start regridding process:'
for i in xrange(varn.shape[0]):
z = g((np.ravel(lat1), np.ravel(lon1)),
varn_re[i, :], (lats, lons),
method='linear')
varn[i, :, :] = z
print 'Completed: Year %s Regridding---' % (years[i])
print 'Completed: Done regridding process!'
return varn
lat1,lon1,snc1 = piomasReader(directorydata1,'snow',years)
### Select month for data analysis (April)
sitpa = sit1[:,3,:,:]
sncpa = snc1[:,3,:,:]
### Select years for data analysis
yearq = np.where(yearsp >= 2011)[0]
sitpa = sitpa[yearq,:,:]
sncpa = sncpa[yearq,:,:]
#### Find PIOMAS values of CS2 lat/lons
interpsit = []
interpsnc = []
for i in xrange(years.shape[0]):
psit = g((lon1.flatten(),lat1.flatten()),
sitpa[i,:,:].flatten(),(lonvals[i],latvals[i]))
psnc = g((lon1.flatten(),lat1.flatten()),
sncpa[i,:,:].flatten(),(lonvals[i],latvals[i]))
interpsit.append(psit)
interpsnc.append(psnc)
print '\nCompleted: Interpolated year %s!' % years[i]
###########################################################################
###########################################################################
###########################################################################
### Save interpolated PIOMAS data
directorytext = '/home/zlabe/Documents/Projects/CAAthickness/Data/'
for i in xrange(years.shape[0]):
for i in range(days.shape[0]):
filename = directorydata2 + 'jan_2018_%s.nc' % days[i]
data = Dataset(filename, 'r')
sic19[i, :, :] = data.variables['ice_conc'][:]
latold2 = data.variables['lat'][:]
lonold2 = data.variables['lon'][:]
data.close()
sic19[np.where(sic19 == -999)] = np.nan
### Calculate monthly average
sicmean = np.nanmean(sic19, axis=0)
#### Regrid data
ak = g((np.ravel(latold2), np.ravel(lonold2)),
sicmean.ravel(), (lat2, lon2),
method='linear')
def netcdfAlaska(lats, lons, var, directory):
print('\n>>> Using netcdfAlaska function!')
name = 'Alaska_SIC_Jan_2018.nc'
filename = directory + name
ncfile = Dataset(filename, 'w', format='NETCDF4')
ncfile.description = 'January 2018 SIC from OSISAF ' \
'interpolated on grid from' \
'Alaska Sea Ice Atlas'
### Dimensions
ncfile.createDimension('lat', var.shape[0])