

In [1]:

    
%load_ext load_style
%load_style talk.css

Read SST, Mask and Calculate global mean

In this notebook, we will carry out the following basic operations

have a quick visualization of spatial data
use mask array to mask out land
calculate yearly climatology
calculate global mean, simply.

1. Load basic libraries



In [2]:

    
%matplotlib inline
import numpy as np
         
from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/

import matplotlib.pyplot as plt         # to generate plots
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 9

2. Read skt data

2.1 Read data



In [3]:

    
ncfile = 'data\skt.mon.mean.nc'

fh  = Dataset(ncfile, mode='r') # file handle, open in read only mode
skt = fh.variables['skt'][:]
fh.close() # close the file

2.2 Have a dirty look of the first month

You can fine the skt cover both of land and ocean.



In [4]:

    
plt.imshow(skt[0])                        #data for the first time-step (January 1948)
plt.title('Monthly SKT in Jan 1948 [$^oC$]')









    Out[4]:





<matplotlib.text.Text at 0x8421e10>

3. Read mask data

We hope only see skt over ocean. That is so-called (Sea Surface Temperature) SST. So have to mask the land part.

3.1 read data



In [5]:

    
lmfile = 'data\lsmask.19294.nc'
lmset  = Dataset(lmfile)
lsmask = lmset['lsmask'][0,:,:]# read land mask

3.2 Have a dirty look of the mask



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plt.imshow(lsmask)                        
plt.title('Land and Ocean Mask')









    Out[6]:





<matplotlib.text.Text at 0xcb752e8>

3.3 Set "1" on Ocean and "nan" on the land



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lsm = lsmask + 1
lsm[lsm<1.0] = np.nan

# now only ocean available
plt.imshow(lsm)                        
plt.title('Land Mask')









    Out[7]:





<matplotlib.text.Text at 0xcfe9a58>

4. Calculate yearly climatology in the first year (1948)

Get skt for the first year (i.e., 12 months)
Calculate yearly mean
Mask the yearly mean over ocean



In [8]:

    
skt_y1 = np.mean(skt[0:12,:,:], axis=0)

# masking the land, leave ocean alone
sst_y1 = skt_y1*lsm

# dirty look
plt.imshow(sst_y1)                       
plt.title('Yearly Climatology of SST in 1948 [$^oC$]')









    Out[8]:





<matplotlib.text.Text at 0xd26a588>

5. Simply calculate global mean

The following is the global averaged sst value. True only considering regular grid. No weighting needed.



In [9]:

    
sst_global = np.nanmean(sst_y1)
sst_global









    Out[9]:





10.126839

References

http://unidata.github.io/netcdf4-python/

John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), DOI:10.1109/MCSE.2007.55

Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37

Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.



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