Visualize Climate data with Panoply netCDF viewer

Authors: orcid logoAnne Fouilloux avatar Anne Fouilloux
Creative Commons License: CC-BY Questions:
  • How to start Panoply interactive environment in Galaxy?

  • How to inspect netCDF data?

  • How to make a plot with Panoply?

  • Where to save your plots in Panoply?

  • How to customize plots in Panoply?

  • How to generate animation in Panoply?

  • Learn to use Panoply in Galaxy to visuallize netCDF geo-referenced data

  • Learn how Panoply plots are exported to Galaxy

Time estimation: 1 hour
Supporting Materials:
Published: Mar 5, 2020
Last modification: Nov 9, 2023
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
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The practical aims at familiarzing you with the Panoply Galaxy interactive environment. Panoply is among the most popular tool to visualize geo-referenced data stored in Network Common Data Form (netCDF). It provides a graphical interface for inspecting (show metadata) and visualizing netCDF data. It supports many features to customize your plots and we will introduce some of them in this lesson.


This tutorial is significantly based on the Panoply documentation “Quick View Data with Panoply” section.

In this tutorial, you will learn to:

  • Plot geo-referenced latitude-longitude, latitude-vertical, longitude-vertical, time-latitude or time-vertical arrays.
  • Use any of numerous color tables for the scale colorbar
  • Save plots to GIF, JPEG, PNG or TIFF bitmap images or as PDF or PostScript graphics files.
  • Export animations as MOV or AVI videos

In this tutorial, we will cover:

  1. NetCDF format
  2. Get data
  3. Panoply
    1. Opening up Panoply
    2. Inspect metadata
    3. Create Geo-referenced Longitude-Latitude plot
    4. Changing map projection
    5. Export Animation
    6. Create timeseries
    7. Quit Panoply
  4. Conclusion
Comment: Background

There are many online services to get climate data, and it is often difficult to know which ones are up-to date and which resources to trust. Different services provide different Application Programming Interfaces (API), use different terminologies, different file formats etc., which make it difficult for new users to master them all. Therefore in this tutorial, we will be focusing on the usage of Climate data in Network Common data Form (netCDF) because it is the most common data format for storing Climate data. We will be using a freely available dataset containing Essential Climate Variables (sea ice area fraction, surface temperature) from Copernicus Climate Data Store. We will learn to use panoply to visualize the sea ice area fraction over the poles (southern and northern poles) and surface temperatures for two different years (1979 and 2018).

NetCDF format

NetCDF data format is a binary format and to be able to read or visualize it, we would need to use dedicated software or libraries that can handle this “special” format. It is self-describing and machine-independent data format that supports the creation, access, and sharing of array-oriented scientific data. NetCDF files usually have the extension .nc or .netcdf.

For climate and forecast data stored in NetCDF format there are (non-mandatory) conventions on metadata (CF Convention).

In this tutorial, we will be using data from the Copernicus Climate Data Store and more precisely a reanalysis ERA5-Land monthly averaged dataset for 2019. Data from Copernicus Climate Data Store is freely available but may require free registration and license agreement.

Get data

Hands-on: Data upload
  1. Create a new history for this tutorial. If you are not inspired, you can name it Panoply for example…

    To create a new history simply click the new-history icon at the top of the history panel:

    UI for creating new history

  2. Import the file from Zenodo or from the shared data library
    • Copy the link location
    • Click galaxy-upload Upload Data at the top of the tool panel

    • Select galaxy-wf-edit Paste/Fetch Data
    • Paste the link(s) into the text field

    • Press Start

    • Close the window

    As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

    1. Go into Shared data (top panel) then Data libraries
    2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
    3. Select the desired files
    4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu
    5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
    6. Click on Import

  3. Check that the datatype is netcdf

    Files you uploaded are in netcdf format. In Galaxy, Datatypes are, by default, automatically guessed. Here, as netcdf is a derivative of the h5 format, Galaxy automatically affect the h5 datatype to netcdf files. To cope with that, one can change the datatype manually, once datasets uploaded (as shown below) OR you can directly specify datatype on the upload tool form so Galaxy will not try to automatically guess it.

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, click galaxy-chart-select-data Datatypes tab on the top
    • In the galaxy-chart-select-data Assign Datatype, select datatypes from “New type” dropdown
      • Tip: you can start typing the datatype into the field to filter the dropdown menu
    • Click the Save button

  4. Rename Datasets

    As “” is not a beautiful name and can give errors for some tools, it is a good practice to change the dataset name by something more meaningfull. For example by removing to obtain and, respectively.

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, change the Name field
    • Click the Save button

  5. Add a tag to the dataset corresponding to copernicus

    Datasets can be tagged. This simplifies the tracking of datasets across the Galaxy interface. Tags can contain any combination of letters or numbers but cannot contain spaces.

    To tag a dataset:

    1. Click on the dataset to expand it
    2. Click on Add Tags galaxy-tags
    3. Add tag text. Tags starting with # will be automatically propagated to the outputs of tools using this dataset (see below).
    4. Press Enter
    5. Check that the tag appears below the dataset name

    Tags beginning with # are special!

    They are called Name tags. The unique feature of these tags is that they propagate: if a dataset is labelled with a name tag, all derivatives (children) of this dataset will automatically inherit this tag (see below). The figure below explains why this is so useful. Consider the following analysis (numbers in parenthesis correspond to dataset numbers in the figure below):

    1. a set of forward and reverse reads (datasets 1 and 2) is mapped against a reference using Bowtie2 generating dataset 3;
    2. dataset 3 is used to calculate read coverage using BedTools Genome Coverage separately for + and - strands. This generates two datasets (4 and 5 for plus and minus, respectively);
    3. datasets 4 and 5 are used as inputs to Macs2 broadCall datasets generating datasets 6 and 8;
    4. datasets 6 and 8 are intersected with coordinates of genes (dataset 9) using BedTools Intersect generating datasets 10 and 11.

    A history without name tags versus history with name tags

    Now consider that this analysis is done without name tags. This is shown on the left side of the figure. It is hard to trace which datasets contain “plus” data versus “minus” data. For example, does dataset 10 contain “plus” data or “minus” data? Probably “minus” but are you sure? In the case of a small history like the one shown here, it is possible to trace this manually but as the size of a history grows it will become very challenging.

    The right side of the figure shows exactly the same analysis, but using name tags. When the analysis was conducted datasets 4 and 5 were tagged with #plus and #minus, respectively. When they were used as inputs to Macs2 resulting datasets 6 and 8 automatically inherited them and so on… As a result it is straightforward to trace both branches (plus and minus) of this analysis.

    More information is in a dedicated #nametag tutorial.


Opening up Panoply

Hands-on: Launch Panoply

Panoply is available as a Galaxy interactive environment and may not be available on all Galaxy servers.

Currently Panoply in Galaxy is available on instance, on the “Interactive tools” tool panel section or, as all interactive tools, from the dedicated subdomain:

  1. Open the Panoply on
  2. Check dataset selected in the netcdf input field
  3. Click Run Tool
  4. The tool will start running and will stay running permanently
  5. Click on the “User” menu at the top and go to “Active Interactive Tools” and locate the Panoply instance you started.
  6. Click on your Panoply instance
    Panoply dataset selection. Open image in new tab

    Figure 1: Select dataset
  7. Click on dataset

Inspect metadata

Hands-on: Inspect dataset
  1. Inspect dataset content

    Here you can look at the dataset ( and related variables (latitude, longitude, number, siconc, etc)


    what is the unit of the sea ice area fraction variable (siconc) variable?

    The unit of siconc is (0-1) e.g. a real value between 0 (no ice) and 1 (fully coverted by ice).

  2. Inspect the surface temperature (t2m) variable


    what is the unit of this variable and its shape?

    The unit of t2m is Kelvin (K). t2m is a 3D array (time, latitude, longitude).

Create Geo-referenced Longitude-Latitude plot

Hands-on: geographical map
  1. Double click on the variable t2m and click on Create
    Panoply create Latitude longitude map. Open image in new tab

    Figure 2: Create map
    1. What does it show?
    2. What is the date of the generated plot?
    3. Can you plot other dates?
    1. The plot represent the surface temperature over the entire world.
      Panoply Latitude longitude map. Open image in new tab

      Figure 3: Plot map
    2. The date of the default plot is 1st January 1979 at 00:00:00.

    3. To plot another date, change either:
      • Initial time of forecast (give a value between 1 and 12, corresponding to each month of year 1979.
      • Click on the date and scroll down to select the date of your choice.
  2. Save your plot
    • Click on the tab File (from your plot window) to store your plot by selecting Save Image As
    • Double click on the folder outputs to enter this folder and save your plot. You need to make sure to save all your plot in the outputs folder otherwise you can loose all your plots once to close panoply.
  3. Change colormap Always make sure you use color blind friendly palettes.
    • To change the default colormap, click on tab “Scale” (bottom of your plot window) and select another “Color Table” (you can scroll down to go through all the different available colormap).
    • Save your plot using Save Image As and make sure to choose another name to avoid overwritting your preceding plot.
    Panoply colormap. Open image in new tab

    Figure 4: Plot colormap

Changing map projection

Hands-on: Change projection
  1. From your previous plot window, click on Tab Map and change Projection. Try a few of them and save each of your plot with File –> Save Image As.

    Panoply change projection. Open image in new tab

    Figure 5: Change projection
  2. Create another plot window for sea ice area fraction (siconc) and make a new geo-referenced map

    1. What kind of colormap could you use to highlight the extent of sea-ice?
    2. What projection would be best to use for showing the extent of sea-ice over the two poles?
    1. Any colormap that shows low values (close to 0) in light color so we can focus on values that are close to 1. For instance, CP_PuBu_08.cpt.
      Panoply sea-ice colormap. Open image in new tab

      Figure 6: Sea-ice colormap
    2. Using Orthographic projection is best for showing the northern and southern poles. One advantage is that you can choose to center the plot over 90 degrees latitude. To have both the northern and southern poles at the same time, choose Stereographic (Two hemispheres).
    Panoply ortho plot. Open image in new tab

    Figure 7: Plot sea-ice using orthographic projection

Export Animation

Hands-on: Export animation
  1. From your previous plot window, click on File and select Export Animation. Save your plot using either MOV or AVI format. It goes through each plot e.g. for each month and create an animation where you can see the evolution of sea-ice extent from January 1979 to December 1979. You will be able to download the resulting movie from Galaxy once you quit Panoply.

Create timeseries

Hands-on: Create 1D plot
  1. Double click on the variable t2m, click on Create and select Create horizontal line plot along time axis (make sure to switch to time).
Panoply create 1D plot. Open image in new tab

Figure 8: Create 1D plot
  1. What was the maximum temperature in Oslo (latitude: 60 degrees North, longitude: 10.75 East) in 1979?
  2. Which month was the warmest in Oslo?
  1. The maximum temperature is about 288 K so about 15 degrees Celsius (288 - 273.15).
  2. The warmest month in 1979 was July.
Panoply 1D plot. Open image in new tab

Figure 9: Plot surface temperature Oslo

Quit Panoply

Hands-on: Quit Panoply to keep your plots
  1. To make sure all your plots stored in outputs folder get exported to Galaxy, you need to quit panoply: File –> Quit Panoply.
  2. Go back to your current Galaxy history and you should find Panoply outputs
Panoply outputs. Open image in new tab

Figure 10: Panoply outputs in Galaxy


We have now learnt how to analyze climate data using Panoply. We only use one of the two datasets so we strongly encourage you to do the same exercises with the second dataset Please note that when comparing surface temperature or sea-ice area fraction from 1979 and 2018, you would not be able to conclude anything regarding climate change. For any climate studies, long term timeseries (between 20 to 30 years) are necessary to establish climate trends.