Introduction

Comment

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

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.

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

Agenda

In this tutorial, we will cover:

1. Introduction
   1. NetCDF format
   2. Get data
2. 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
3. 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…

   Tip: Creating a new history

   Click the new-history icon at the top of the history panel.

   If the new-history is missing:

   1. Click on the galaxy-gear icon (History options) on the top of the history panel
   2. Select the option Create New from the menu

2. Import the file from Zenodo or from the shared data library

   https://zenodo.org/record/3697454/files/ecv_1979.nc
   https://zenodo.org/record/3697454/files/ecv_2018.nc
   

   Tip: Importing via links

   • Copy the link location

   • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

   • Select Paste/Fetch Data

   • Paste the link into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   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:

   • Go into Shared data (top panel) then Data libraries
   • Navigate to the correct folder as indicated by your instructor
   • Select the desired files
   • Click on the To History button near the top and select as Datasets from the dropdown menu
   • In the pop-up window, select the history you want to import the files to (or create a new one)
   • 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 necdf 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.

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
   • Select datatypes
     • tip: you can start typing the datatype into the field to filter the dropdown menu
   • Click the Save button

4. Rename Datasets

   As “https://zenodo.org/record/3697454/files/ecv_1979.nc” 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 https://zenodo.org/record/3697454/files/ to obtain ecv_1979.nc and ecv_2018.nc, respectively.

   Tip: Renaming a dataset

   • 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

   Tip: Adding a tag

   • Click on the dataset
   • Click on galaxy-tags Edit dataset tags

   • Add a tag starting with #

     Tags starting with # will be automatically propagated to the outputs of tools using this dataset.

   • Check that the tag is appearing below the dataset name

Panoply

Opening up Panoply

Hands-on: Launch Panoply

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

Tip: Launch Panoply in Galaxy

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

1. Open the Panoply Tool: interactive_tool_panoply on UseGalaxy.eu
2. Check ecv_1979.nc dataset selected in the netcdf input field
3. Click Execute
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

   

7. Click on ecv_1979.nc dataset

Inspect metadata

Hands-on: Inspect dataset

1. Inspect dataset content

   Here you can look at the dataset (ecv_1979.nc) and related variables (latitude, longitude, number, siconc, etc)

   Question

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

   Solution

   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

   Question

   what is the unit of this variable and its shape?

   Solution

   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

   

   Question

   1. What does it show?
   2. What is the date of the generated plot?
   3. Can you plot other dates?

   Solution

   1. The plot represent the surface temperature over the entire world.

      

   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 wind) 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.

   

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.

   

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

   Question

   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?

   Solution

   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.

      

   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).

   

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).

Question

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?

Solution

1. The maximum temperature is about 288 K so about 15 degrees Celsius (288 - 273.15).
2. The warmest month in 1979 was July.

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

Conclusion

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 ecv_2018.nc. 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.

Key points

• Inspect and view netCDF data with Panoply

• Interact with Galaxy to save your plots

Frequently Asked Questions

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Climate topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

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Citing this Tutorial

1. Anne Fouilloux, 2022 Visualize Climate data with Panoply netCDF viewer (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/climate/tutorials/panoply/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{climate-panoply,
author = "Anne Fouilloux",
title = "Visualize Climate data with Panoply netCDF viewer (Galaxy Training Materials)",
year = "2022",
month = "10",
day = "21"
url = "\url{https://training.galaxyproject.org/training-material/topics/climate/tutorials/panoply/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}
}
                   

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