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Fix typos in example notebooks (#4908)
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doc/examples/ERA5-GRIB-example.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
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"GRIB format is commonly used to disseminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
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doc/examples/ROMS_ocean_model.ipynb

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"### A naive vertical slice\n",
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"\n",
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"Create a slice using the s-coordinate as the vertical dimension is typically not very informative."
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"Creating a slice using the s-coordinate as the vertical dimension is typically not very informative."
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doc/examples/monthly-means.ipynb

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"cell_type": "markdown",
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"Calculating Seasonal Averages from Timeseries of Monthly Means \n",
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"Calculating Seasonal Averages from Time Series of Monthly Means \n",
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"=====\n",
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"\n",
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"Author: [Joe Hamman](https://github.com/jhamman/)\n",
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"source": [
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"#### Now for the heavy lifting:\n",
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"We first have to come up with the weights,\n",
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"- calculate the month lengths for each monthly data record\n",
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"- calculate the month length for each monthly data record\n",
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"- calculate weights using `groupby('time.season')`\n",
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"\n",
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"Finally, we just need to multiply our weights by the `Dataset` and sum allong the time dimension. Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate. The calendar type, in this case `'noleap'`, is automatically considered in this operation."
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"Finally, we just need to multiply our weights by the `Dataset` and sum along the time dimension. Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate. The calendar type, in this case `'noleap'`, is automatically considered in this operation."
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