Subtract, Average, Squeeze, then Subset Variable in NetCDF via Python

Question

I have a NetCDF that has multiple pieces pollution data across U.S. in a daily value format. I am hoping to subtract a background variable ('nosmoke background PM25') from another ('PM25') then average this result to create an annual value. From what I understand, by creating an annual value I change my array from three dimensions (time, lat, long) to two (lat, long) since there is now no daily values. I am then hoping to subset this variable to only include a region of interest. Below is my code. Any help is much appreciated.

import netCDF4 as nc
import numpy as np
 
fn = '24hr_SmokePM25_2018.nc'
ds = nc.Dataset(fn)

#All data defining total PM25
TOT_PM25 = ds.variables['PM25']
NS_BKGD_PM25 = ds.variables['nosmoke background PM25']

#create empty array that is same size as TOT_PM25 variable to fill
Smoke_PM25 = np.full_like(TOT_PM25,0)

#For loop in subtracting total PM25 from no smoke PM25
for i in range (1,365):
    Smoke2_PM25[i,:,:]=TOT_PM25[i,:,:]-NS_BKGD_PM25[i,:,:]

#Averaging and squeezing daily value
np.mean(Smoke2_PM25[i,:,:])
np.squeeze(Smoke2_PM25, axis=0)

#boundary input for desired region to pull
latbounds = [ 24 , 39 ]
lonbounds = [ -96 , -77 ]
lons = ds.variables['lon'][:]
lats = ds.variables['lat'][:]

# latitude lower and upper index
latli = np.argmin( np.abs( lats - latbounds[0] ) )
latui = np.argmin( np.abs( lats - latbounds[1] ) ) 

# longitude lower and upper index
lonli = np.argmin( np.abs( lons - lonbounds[0] ) )
lonui = np.argmin( np.abs( lons - lonbounds[1] ) )  

# Value subsets (SmokeP25, latitude, longitude) 
Smoke_Region_PM25 = ds.variables['PM25'][ : , latli:latui , lonli:lonui ] 

#Close the NetCDF
ds.close()

Robert Wilson · Accepted Answer · 2021-02-11 10:31:19Z

1

You can do this easily with v0.3.0 of my nctoolkit package (https://pypi.org/project/nctoolkit/).

First read the data in:

import nctoolkit as nc
fn = '24hr_SmokePM25_2018.nc'
data = nc.open_data(fn)

Then subtract one variable from the other to create your new variable:

data.assign(new = lambda x: x.PM25 - x.nosmoke_background_PM25)

Once that is done you can calculate an annual mean:

data.tmean("year")

Then visualize the results:

data.plot("new")

answered Feb 11, 2021 at 10:31

Robert Wilson

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