Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How can I work with gridded datasets

Objectives

• Downloading satellite data from ERDDAP in netCDF format

• Extracting data with Python

• Map spatial datasets

Working with gridded data

Gridded data works with a different protocol called griddap. Instead of using the erddapy library. It is easier to import the data in .netcdf using the package xarray and netcdf.

Setting the constraints with this packageg is a bit more straightforward

griddap request URLs must be in the form https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}

For example, https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]

The query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable’s dimensions (for example, [time][latitude][longitude]).

Link to static Jupyter Notebook. Copy/Paste the code blocks into your own Jupyter Notebook

xarray works similarly as the pandas data package.

import xarray as xr
import netCDF4 as nc

Satellite data NASA sea surface temperature : GHRSST Global 1-km Sea Surface Temperature (G1SST), Global, 0.01 Degree, 2010-2017, Daily The data contains daily composites of SST with 1 km resolution

Importing the downloaded data in Python. Now that we’ve downloaded the data locally, we can import it and extract our variables of interest:

import xarray as xr

server = 'https://coastwatch.pfeg.noaa.gov/erddap'
protocol = 'griddap'
dataset_id = "jplG1SST"
full_URL = '/'.join([server,protocol,dataset_id])
print(full_URL)
da = xr.open_dataset(full_URL)

Inspect the dataframe:

print (da)

Getting the dataset without subsetting it creates an error -> the data set is too large to be downloaded completely.

sst = da['SST']
sst

Create subsets of your netcdf file:

For this exercise, the area we are interested in includes Monterey Bay, CA:

• Latitude range: 44.0N, 48.0N
• Longitude range: -128E, -121E
• Time range 2017-01-13T00:00:00Z to 2017-01-16T23:59:59Z

Xarray supports:

• label-based indexing using .sel
• position-based indexing using .isel

slice() function can take three parameters:

• start (optional) - Starting integer where the slicing of the object starts. Default to None if not provided.
• stop - Integer until which the slicing takes place. The slicing stops at index stop -1 (last element)

import xarray as xr

server = 'https://coastwatch.pfeg.noaa.gov/erddap'
protocol = 'griddap'
dataset_id = "jplG1SST"
full_URL = '/'.join([server,protocol,dataset_id])
print(full_URL)
da = xr.open_dataset(full_URL)


sst = da['SST'].sel(  
                  latitude=slice(44., 48.),  
                  longitude=slice(-128, -121), 
                  time='2017-01-13T00:00:00'
                 )
sst

%matplotlib inline
sst.isel(time=0).plot.imshow()

Key Points

• There are key packages necessary to import data from ERDDAP into Python: xarray

• xarray works similar to Pandas

• xarray has a build in plotter for gridded datasets