Manuscript

Author

Matt Fisher, Trey Stafford

I’m a manuscript and I’m OK

import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)

line = plt.plot(t, s, lw=2)
plt.show()

Figure 1: Plot data with matplotlib

I’m black and white and read all day

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Pangeo-Forge

Pangeo Forge provides efficient, cloud-optimized access to large datasets.

Using xarray

Lets use xarray to access Pangeo Forge’s NOAA Coastwatch Geo-Polar SST dataset. This dataset was added to Pangeo Forge via a recipe maintained in a pangeo-forge feedstock repository from data hosted at NOAA (Coastwatch 2023).

import xarray as xr

store = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/noaa-coastwatch-geopolar-sst-feedstock/noaa-coastwatch-geopolar-sst.zarr'
ds = xr.open_dataset(store, engine='zarr', chunks={})

Figure 2: ?(caption)

Selecting data

Lets select a slice (2012-09-16, the date of the record sea ice minimum per the Sea Ice index) out of the sea surface temperature (sst) variable.

sst_20120916 = ds.analysed_sst.sel(time='20120916', method='nearest')

Figure 3: ?(caption)

Plotting data

Now lets take a look at the data!

import matplotlib.pyplot as plt

img = sst_20120916.plot.imshow()
plt.show()

Figure 4: Plot the results.

Remember how we already plotted some data with matplotlib earlier? See Figure 1!

References

Coastwatch, NOAA. 2023. “NOAA Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4).” March 2023. https://nsidc.github.io/quarto-demo.