Tutorial 3: Opening and Plotting netCDF Data#

Week 1, Day 1, Climate System Overview

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Content editors: Jenna Pearson, Chi Zhang, Ohad Zivan

Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan

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Pythia credit: Rose, B. E. J., Kent, J., Tyle, K., Clyne, J., Banihirwe, A., Camron, D., May, R., Grover, M., Ford, R. R., Paul, K., Morley, J., Eroglu, O., Kailyn, L., & Zacharias, A. (2023). Pythia Foundations (Version v2023.05.01) https://zenodo.org/record/8065851

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Tutorial Objectives#

Many global climate datasets are stored as NetCDF (network Common Data Form) files. NetCDF is a file format for storing multidimensional variables such as temperature, humidity, pressure, wind speed, and direction. These types of files also include metadata that gives you information about the variables and dataset itself.

In this tutorial, we will import atmospheric pressure and temperature data stored in a NetCDF file. We will learn how to use various attributes of Xarray to import, analyze, interpret, and plot the data.

Setup#

# imports
import numpy as np
import pandas as pd
import xarray as xr
from pythia_datasets import DATASETS
import matplotlib.pyplot as plt

Figure Settings#

# @title Figure Settings
import ipywidgets as widgets  # interactive display

%config InlineBackend.figure_format = 'retina'
plt.style.use(
    "https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle"
)

Video 1: Atmospheric Climate Systems#

Tutorial slides#

These are the slides for the videos in all tutorials today

Section 1: Opening netCDF Data#

Xarray is closely linked with the netCDF data model, and it even treats netCDF as a ‘first-class’ file format. This means that Xarray can easily open netCDF datasets. However, these datasets need to follow some of Xarray’s rules. One such rule is that coordinates must be 1-dimensional.

Here we’re getting the data from Project Pythia’s custom library of example data, which we already imported above with from pythia_datasets import DATASETS. The DATASETS.fetch() method will automatically download and cache (store) our example data file NARR_19930313_0000.nc locally.

filepath = DATASETS.fetch("NARR_19930313_0000.nc")

Once we have a valid path to a data file that Xarray knows how to read, we can open it like this:

Questions 1#

What are the dimensions of this dataset?
How many climate variables are in this dataset?

# to_remove explanation

"""
1. time1, isobaric1 (pressure), y, x. Note that y and x are coordinates for the lat and lon variables.
2. 4.
""";

Section 2: Plotting with Xarray#

Another major benefit of using labeled data structures is that they enable automated plotting with axis labels.

Section 2.1: Simple Visualization with `.plot()`#

Much like Pandas, Xarray includes an interface to Matplotlib that we can access through the .plot() method of every DataArray.

For quick and easy data exploration, we can just call .plot() without any modifiers:

prof.plot()

[<matplotlib.lines.Line2D at 0x7f9d39d96d10>]

../../../_images/W1D1_Tutorial3_33_1.png

Here Xarray has generated a line plot of the temperature data against the coordinate variable isobaric. Also, the metadata are used to auto-generate axis labels and units.

Consider the following questions:

What isobaric pressure corresponds to Earth’s surface?
How does temperature change with increasing altitude in the atmosphere?

It might be a bit difficult to answer these questions with our current plot, so let’s try customizing our figure to present the data clearer.

Section 2.2: Customizing the Plot#

As in Pandas, the .plot() method is mostly just a wrapper to Matplotlib, so we can customize our plot in familiar ways.

In this air temperature profile example, we would like to make two changes:

swap the axes so that we have isobaric levels on the y (vertical) axis of the figure (since isobaric levels correspond to altitude)
make pressure decrease upward in the figure, so that up is up (since pressure decreases with altitude)

We can do this by adding a few keyword arguments to our .plot():

prof.plot(y="isobaric1", yincrease=False)

[<matplotlib.lines.Line2D at 0x7f9d37701e10>]

../../../_images/W1D1_Tutorial3_36_1.png

Questions 2.2#

What isobaric pressure corresponds to Earth’s surface?
Why do you think temperature generally decreases with height?

# to_remove explanation

"""
1. About 1000 hPa.
2. Temperature decreases with increasing altitude primarily as a result of the decreasing air pressure, as dictated by the ideal gas law and the adiabatic cooling effect. According to the ideal gas law, which states that the pressure of a gas is directly proportional to its temperature when the volume and the amount of gas stay the same, the decrease in air pressure at higher altitudes leads to a decrease in temperature. Additionally, as air rises, it expands due to the decrease in pressure, resulting in a drop in temperature due to reduced kinetic energy of the air molecules.

""";

Section 2.3: Plotting 2D Data#

In the example above, the .plot() method produced a line plot.

What if we call .plot() on a 2D array? Let’s try plotting the temperature data from the 1000 hPa isobaric level (surface temperature) for all x and y values:

temps.sel(isobaric1=1000).plot()

<matplotlib.collections.QuadMesh at 0x7f9d3757bbb0>

../../../_images/W1D1_Tutorial3_41_1.png

Xarray has recognized that the DataArray object calling the plot method has two coordinate variables, and generates a 2D plot using the pcolormesh method from Matplotlib.

In this case, we are looking at air temperatures on the 1000 hPa isobaric surface over North America. Note you could improve this figure further by using Cartopy to handle the map projection and geographic features.

Questions 2.2: Climate Connection#

The map you made is showing temperature across the United States at the 1000 hPa level of the atmosphere. How do you think temperatures at the 500 hPa level would compare? What might be causing the spatial differences in temperature seen in the map?

# to_remove explanation

"""
1. In general, we would expect temperatures at the 500 hPa level (which is higher in the atmosphere) to be cooler than temperatures at the 1000 hPa level. The spatial differences in temperature seen in the map could be caused by a variety of factors, including solar radiation received at different latitudes, atmospheric circulation patterns, elevation difference, geographical features like mountains and bodies of water, and human activity.
""";

Summary#

Xarray brings the joy of Pandas-style labeled data operations to N-dimensional data. As such, it has become a central workhorse in the geoscience community for analyzing gridded datasets. Xarray allows us to open self-describing NetCDF files and make full use of the coordinate axes, labels, units, and other metadata. By utilizing labeled coordinates, our code becomes simpler to write, easier to read, and more robust.

Resources#

Code and data for this tutorial is based on existing content from Project Pythia.

Climatematch Academy: Computational Tools for Climate Science

Tutorial 3: Opening and Plotting netCDF Data

Contents

Tutorial 3: Opening and Plotting netCDF Data#

#

#

Tutorial Objectives#

Setup#

Figure Settings#

Video 1: Atmospheric Climate Systems#

Tutorial slides#

Section 1: Opening netCDF Data#

Questions 1#

Section 1.1: Subsetting the `Dataset`#

Section 1.2: Aggregation Operations#

Section 2: Plotting with Xarray#

Section 2.1: Simple Visualization with `.plot()`#

Section 2.2: Customizing the Plot#

Questions 2.2#

Section 2.3: Plotting 2D Data#

Questions 2.2: Climate Connection#

Summary#

Resources#

Climatematch Academy: Computational Tools for Climate Science

Tutorial 3: Opening and Plotting netCDF Data

Contents

Tutorial 3: Opening and Plotting netCDF Data#

#

#

Tutorial Objectives#

Setup#

Figure Settings#

Video 1: Atmospheric Climate Systems#

Tutorial slides#

Section 1: Opening netCDF Data#

Questions 1#

Section 1.1: Subsetting the Dataset#

Section 1.2: Aggregation Operations#

Section 2: Plotting with Xarray#

Section 2.1: Simple Visualization with .plot()#

Section 2.2: Customizing the Plot#

Questions 2.2#

Section 2.3: Plotting 2D Data#

Questions 2.2: Climate Connection#

Summary#

Resources#

Section 1.1: Subsetting the `Dataset`#

Section 2.1: Simple Visualization with `.plot()`#