The Detroit Post
Thursday, 28 October, 2021

Best Weather Visualization

Carole Stephens
• Wednesday, 18 November, 2020
• 11 min read

The embodies great because it aggregates and archives almost every text product issued by the National Weather Service in real time. The site also features a mammogram generator (an example is pictured above) that allows you to visualize model data for certain variables such as temperature and rain/snowfall for up to a week in the future.



The site is great for tracking synoptic scale features such as nor'Easters and the jet stream. SimuAWIPS, pictured at the top of this post, is an incredible resource for tracking current weather and predicting what will happen in the short- to medium-range.

The SimuAWIPS interface is interactive, customizable, and allows you to view up to four windows of weather data at once. Its features, layout, and interactivity make it my number one site for weather graphics.

A good data visualization is made up of several components that have to be pieced up together to produce an end product: b) Geometric Component: Here is where you decide what kind of visualization is suitable for your data, e.g. scatter plot, line graphs, bar plots, histograms, plots, smooth densities, box plots, pair plots, heatmaps, etc.

f) Ethical Component : Here, you want to make sure your visualization tells the true story. This article will compare the strengths of Python’s Matplotlib and R’s ggplot2 package for analyzing and visualizing weather data.

This data comes from a subset of the National Centers for Environmental Information (CEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily comprises daily climate records from thousands of land surface stations across the globe.


I was wondering, can I produce a similar visualization of the figure above using R’s ggplot2 package? It seems to me that for this particular project, Matplotlib produces a better and more beautiful visualization compared to R’s ggplot2 package.

Using matplotlib, the visualization was produced with fewer effort (lines of code), compared to ggplot2. Please leave feedback comments for suggestions on how to improve the ggplot2 visualization.

Data Snapshots offers a range of easy-to-understand climate maps with plain English descriptions. Each set of snapshots is a simplified version of official climate products available across a number of different Websites.

NOAA View is an introductory data viewer suitable for a broad range of audiences. The tool's online interface makes it easy to explore time-series image maps of global data.

Example datasets: wind speed, coral bleaching, ice cover, vegetation, precipitation, and views of Earth at night. You can also display specific dates, animate time series, and download .png images or Km (Google Earth) files.

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Panoply is a data viewer designed to work with net CDF, HDF, Grid and other datasets. Save plots to disk GIF, JPEG, PNG or TIFF bitmap images or as PDF or Postscript graphics files.

Export animations as AVI or Move video or as a collection of individual frame images. NOAA's Weather and Climate Toolkit (WCT) is free, platform independent software.

The WCT provides tools for background maps, animations and basic filtering. Each day, we process terabytes of data for use with our Meriwether API and Meriwether Mapping Platform and are always looking for improved ways to visualize all the data.

D3.js is one of the most popular data visualization libraries for the web. By combining D3.js with other open-source libraries such as Cross filter and DC.js, as well as our own Meriwether API, some great weather data visualizations are possible.

Last month there were just under 11,000 storm reports across the US, which includes data on tornadoes, hail size, snowfall, rainfall and more. Visualizing such a large amount of data in a clean and understandable way can be a challenge, yet it’s the perfect opportunity for an interactive data visualization.


Our interactive storm reports page allows viewing reports across a date range and selecting an area on the maps and charts to filter the dataset. This type of visualization can be quite useful in reviewing not only the types of severe weather and storm damage but also the progression of the damage across a specific time period.

To create the storm reports visualization, you’ll need access to our Meriwether API for the data and will be utilizing the following open-source JavaScript libraries: D3.js : A JavaScript library that can render interactive charts and graphics based on data.

Crossfilter.js : A JavaScript library for processing large datasets, providing fast interaction between coordinated views. DC.js : A JavaScript wrapper for plotting graphs, that acts as the glue between D3.js and Cross filter.

To demonstrate how easily interactive data visualizations can be created, we’ll step through a simple example whose source is also available on GitHub. If you have not done so already, sign up for a free Meriwether API Developer account.

We will be fetching the storm reports from the Aegis API via the search action, requesting up to 1000 storm reports from the past week, sorted newest to oldest. Next, we will utilize D3’s queue() function which allows asynchronous loading of both the map outline data and the storm reports information from the Aegis API.


Once we have the storm reports data, we step through and transform the elements to be used with the charts, specifically formatting the category name and converting the timestamp to a Date object: Cross filter acts as a two-way data pipeline so that when you make a selection or filter on the data in one dimension/group, it is automatically applied to all other groups and charts.

This creates a live, interconnected ‘on the fly’ update to all the visualizations on the page: Now that we have our storm reports processed in Cross filter, we can set up the charts using DC.js.

For each chart, we provide the necessary configuration properties, such as data dimension, group, axes, width, height, etc: // data count dataCountChart.dimension(facts).group(all); // category chart // calculate the max Count to set the domain of the x-axis.

Var maxTypeCount = d3.max(reportCatGroup.all().map(function (d, i) {return d.value;})) + 25; catRowChart.height(275).margins({top: 5, right: 10, bottom: 20, left: 5}).transitionDuration(1000).dimension(report).group(reportCatGroup).colors(function (d) {return stormReportColorScale(d);}).x(d3.scale.sort().domain().range()).elastic(false); catRowChart.axis().ticks(5); ... // calculate the max Count to set the domain of the x-axis.

Expanding on this example, you can customize the charts further using CSS and the available properties within DC.js. Additionally, DC.js offers many other chart types beyond those used in the example.

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As you move forward with interactive data visualizations, you will find that many of the other datasets or combinations of datasets, available within our Meriwether API are perfect candidates for visualization. For example, our active storm cells visualization utilizes the storm cells endpoint of the Meriwether API.

Using the visualization techniques demonstrated in the examples above, you can quickly view which states have the most activity, whether there are any tornado systems, or the various hail sizes and their storm cell locations. Visualization can make otherwise ordinary information such as temperature and wind observations, earthquakes, record highs and lows, and observation/summaries really come alive.

We suggest you click on the links that interest you to see the projects in their full glory. The Codex Atlantic us won the 2019 Gold Kan tar Information is Beautiful Award in the Art and Entertainment category.

For history and Leonardo da Vinci buffs, this digital library is a rich resource for education and research. This visualization about how and where bioluminescence is present on the Southeastern coast of Australia is a great example.

Where the Wild Things Grow is a Tableau visualization created for National Geographic by Joni Walker. The bioluminescence is visualized as shiny shapes over a satellite image of the area.

Another amazing data visualization published by National Geographic in 2019 is The Atlas of Moons. Not many people can manage to create beautiful things with unfortunate data and information.

This visualization shows the piles of plastic bottles in relation to famous landmarks and cityscapes. The first Starbucks Reserve Roastery in Milan hired a group of skilled data visualization artists from the agency, Accurate.

The wall was created in 2018, but it won a Gold Kan tar is Beautiful Award in 2019 for the unusual category. The study includes insightful dialogue between a space industry reporter and a fashion critic.

The basis of the study is that not all stadiums are round, but rather a collection of uneven shapes. Each stadium is visualized first with a satellite image from Google Maps and then graphics with data about the length and width of the field and a detailed representation of the shape.

The Many Shapes of England’s Cricket Stadiums is a visualization for the BBC Sports edition. Data visualization and information design is the type of work that takes a long time to complete.

The Symbolic data visualization, by Michel Grazing, is a large collection of artistically rendered symbols from different cultures and times in history. The data sets are separated into different categories; the distance from the earth, the types of space junk, and the size and mass of the objects.

The Oberhausen design agency created this visualization to show data about notable and memorable space activity that will happen in 2020 and some historical events added on. It has long been a cult book for writers, readers, and conceptual artists alike.

The result is a beautifully printed rendition of the original text plus a twin book with a collection of data visualizations. Graphic designer and data visualization artist Gabrielle Merit took it upon herself to create a data visualization about how the LGBT community is unprotected by specific laws in the United States.

This type of visualization is worth sharing to show just how unprotected the LGBT community is in many of the states in the country. The colors are inspirational, and the inclusion of black and white images with conceptual labeling completes the design.

Georgia’s sketches used the data from each woman’s notable projects to create reusable graphics that were then printed on handmade fabric. The garments are sold inside a designed bag where the data and the inspiration are explained.

David McCandless, the creator of the Kan tar Information is Beautiful Awards, has a great new project this year. In the span of a year, David and his team have collected 300+ datasets in different categories and have created visualizations about them.

Beautiful News Daily publishes a new visualization every day and will do so throughout the year. The project can be viewed on desktop, but the best way is to download the app on a mobile device.

Each hope is represented by a colorful stone, and they are organized into a rotating sculpture. Once the sculpture is ready, you can explore your data and how it compares to the Google Trends in your area.

Market Café Mag is the only ‘zine about data viz and it just won a Gold Kan tar Information is Beautiful award. This year’s issue, #5, is titled Activism in Data Viz and features work by Sonja Kippers, The Pudding, Paul Button and more.

The Kan tar Information is Beautiful Awards brought us another exceptional data visualization from National Geographic. The print version shows a selection of countries, but the full visualization can be navigated on the National Geographic website.

The black and white waves over the yellow background create a wonderfully visual rendition of the data. The South China Morning Post is known for its creative approach to data journalism.

The data journalism team is full of great ideas and is constantly recognized. Although this data visualization was published towards the end of 2018, it won a Kan tar Information is Beautiful award in the Politics and Global category in 2019.

Five Reuters journalists each took on a particular aspect of the crisis and created a complex data viz with charts, video introductions and lots of insight. This data visualization project was part of the XXII International Exhibition of La Triennial di Milano in 2019.

Last year in 2018, The Economist print version started a new section for their Graphic Detail series. This year, the team put together all the Graphic Series publications together into one big downloadable PDF.

If you're interested in making your own data visualizations, head over to Vise's easy-to-use graph maker. Additionally, you can check out this video to help you better understand how to use data visualization to improve your business reports and presentations.

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