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Python Tutorial 3 - Extract data within a shapefile using ERDDAP-2

This tutorial will teach you how to extract and display SST values for a particular time period or average SST over the whole time-series available within a shapefile.

The shapefile for the NOAA Marine National Monument and sanctuaries boundaries can be downloaded here:
http://sanctuaries.noaa.gov/library/imast_gis.html Links to an external site..

We are going to extract SST data for the Papahanaumokuakea Marine National Monument (PMNM) in Hawaii. However, because the Monument boundaries cross the dateline, the shapefile provided on the website is tricky to work with. We'll work with a cleaned up version, available here:
https://oceanwatch.pifsc.noaa.gov/files/PMNM_bounds.csv Links to an external site.

This tutorial is also available as a Jupyter notebook Links to an external site..

 

Load packages

import pandas as pd
import numpy as np
import urllib.request
import xarray as xr
import netCDF4 as nc
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from shapely.geometry import Point, Polygon
import geopandas as gpd

np.warnings.filterwarnings('ignore')

Load the Monument boundary

df=pd.read_csv('PMNM_bounds.csv')

Transform the boundary to a Polygon

geometry = [Point(xy) for xy in zip(df.lon, df.lat)]

poly = Polygon([(p.x, p.y) for p in geometry])

poly

Data extraction

The example below extracts monthly 5km CoralTemp SST Links to an external site. data within the monument boundary.

  • We are going to download data from ERDDAP for the smallest bounding box that contains our polygon

xcoord1 = (np.min(df.lon), np.max(df.lon))
ycoord1 = (np.min(df.lat), np.max(df.lat))

  • let's select a date range:

tcoord = ("2019-01-15", "2019-12-15")

  • and let's build our ERDDAP URL:

url='https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_monthly.nc?analysed_sst[('+ Links to an external site. tcoord[0] +'):1:('+ tcoord[1] +')][('+ str(ycoord1[0]) +'):1:('+ str(ycoord1[1]) +')][(' + str(xcoord1[0]) +'):1:('+ str(xcoord1[1]) +')]'

url

'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_monthly.nc?analysed_sst[(2019-01-15):1:(2019-12-15)][(19.2345832):1:(31.79786423)][(177.84422):1:(198.9827)]'

  • now we can download the data:

urllib.request.urlretrieve(url, "sst.nc")

  • and load it as an xarray dataset:

ds = xr.open_dataset('sst.nc',decode_cf=False)

ds.analysed_sst.shape

(12, 252, 424)

We now have data for a box around our polygon, for 12 monthly time steps (= 1 year).

Masking the data outside the Monument boundary

The .within() function from the shapelypackage checks if a point is within a polygon. We are using it to create a mask which will take the value 1 within the polygon boundary, and NaN outside.

(This takes about 1min or less to run).

mask=np.empty((len(ds.latitude.values),len(ds.longitude.values))) 
mask[:]=np.NaN 

for i in range(len(ds.latitude.values)): 
   for j in range(len(ds.longitude.values)): 
      p=Point(ds.longitude.values[j],ds.latitude.values[i],) 
      if int(p.within(poly))==1: 
         mask[i,j]=int(p.within(poly))

plt.contourf(ds.longitude,ds.latitude,mask)

We now multiply the SST data we downloaded by the mask values:

SST=ds.analysed_sst*mask

Plotting the data

The extracted data contains several time steps (months) of sst data in the monument boundaries. Let's make a plot of the 4th time step for example.

  • setting up the colormap
np.min(SST),np.max(SST)

array(16.863333), array(28.78)

levs = np.arange(16, 29, 0.05) 
jet=["blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"] 
cm = LinearSegmentedColormap.from_list('my_jet', jet, N=len(levs))

country = gpd.read_file("gz_2010_us_outline_20m.json")

  • plot:
country.plot(figsize=(12,8),color='black') 
plt.xlim(-183,-153) 
plt.ylim(18,32) 
cs=plt.contourf(ds.longitude-360,ds.latitude,SST[3,:,:],levs,cmap=cm) 
cbar=plt.colorbar(fraction=0.022) 
cbar.ax.tick_params(labelsize=12) 
cs.ax.tick_params(labelsize=12) 
plt.title('SST - April 2019', fontsize=20)