by Christopher Rudolph

Hurricane Florence | NASA — *Fig 1. Hurricane Florence as recorded by NASA*

Tropical storms are a category of weather events that create wind and rainfall conditions of varying intensity. These conditions can have high destructive potential depending on intensity, with these storms being classified from tropical depression at the weakest, to hurricane at the most intense. They occur between 5- and 20-degrees latitude when low atmospheric pressure systems cross warm ocean surface temperatures. Depending on conditions, winds can develop from as low as 23 mph to over 157 mph. When these storms meet land, they will often cause property damage and threaten lives due to flooding and wind force before dissipating.

The most dangerous of these storms are classified as hurricanes, which are characterized by exceedingly high wind speeds. Hurricanes are famed across the south-eastern United States for the devastating effects they can have when they reach land such as 2005’s Hurricane Katrina with over $125 billion in damage and over 1800 deaths or 2012’s Hurricane Sandy with $70 Billion in damage and 233 deaths. Due to this, the study and prediction of tropical storm development has remained continually relevant.

Why track tropical storms?

Many of the processes surrounding hurricane development are poorly understood, such as ocean and atmospheric circulation. To better understand these events, efforts have been made to form detailed histories of past tropical storm conditions. The National Oceanic and Atmospheric Administration (NOAA) has created detailed records of tropical storms as far back as the mid 1800’s.

The atmosphere and ocean are 2 of the largest carbon and thermal sinks on Earth. With anthropogenic climate change changing the conditions of these two bodies, there is concern that tropical storm development will change with it, potentially with intensification of these destructive events. A search for periods analogous to forecasted future conditions has emerged in an attempt to predict how tropical storm conditions may change. Paleotempestology is a scientific field that has sought to extend tropical storm records past modern monitoring technology using geological proxies and historical documentary records.

This visualization will represent the frequency of tropical storm activity in the Atlantic as a heat map. Kernel Density values are assigned based on proximity to tropical storm path activity. The higher the value, the more tropical storm activity seen in proximity to the location. Kernel density will be visualized on a 10-year basis, helping to visualize how storm activity over time and the frequency at which these storms may impact coastal communities.

Visualization

Fig. 2 Visualization of tropical storm activity density in the west Atlantic.

Data and Platform

For this project, tropical storm data is visualized using the International Best Track Archive for Climate Stewardship (IBTrACS), a tropical cyclone best track data collection published by NOAA.

ARCGis was selected as the platform that would be used for the visualization. The software was familiar and effective for doing the project’s geoprocessing, and looked promising for the visualization product. ARCGis features robust geoprocessing tools for creating the visualization, and has an animation feature that can produce the video format and implement overlay features such as a timeline and text. As the project developed, the animation tool would be abandoned however in favor of Windows Video Editor for the video as discussed later.

Methods

With data available in shapefile form, importing NOAA’s data into ARCGis was simple. The data on display upon importation is overwhelming with over 120 thousand records displayed as travel paths. Performance is low and there is little to no context to what is being viewed.

Fig. 3 – A map of all tropical storm tracks recorded

Using density geoprocessing and the filtering of data range through time, this will be transformed into something interpretable.

Time

Time was the first filter implemented. In the properties of the layer, time was enabled. Each row has corresponding time fields. In this case, year was used. Implementing this introduced an adjustable filter to the map area in the top right. This slider could be adjusted to narrow down the range.

Fig. 4 – Layer Time properties and the resulting time range filter

While handy on the fly, more precise results for filtering time is found within the Map Time tab, with precision controls available there.

Creating the density view

For creating heatmaps typically the heatmap symbology option is used to create effective density views with time enabled filtering. For this visualization, this approach was not available as the approach was incompatible with the line datatype used. To create a density map, geoprocessing would need to be done using the density toolset. The kernel density toolset was selected. This tool uses a bivariate kernel function for form a weight range surrounding each point. These ranges are then summed to form cell density values for each raster grid point, resulting in a heatmap.

This approach carried some issues for implementation however. In the process of geoprocessing, the tool doesn’t take into account or assign any time data to the output. This meant that the processed layer couldn’t be effectively filtered for the visualization. To work around this, the data was broken into layers by desired year range, then processed, creating a layer for each time window. These layers could still be used to make keyframes and scenes for the animation, though this solution would have some added housekeeping in displaying certain details such as time and legend within the video

Format

As mentioned earlier, the ARCGis animation tools were planned for use as the delivery format. Working with the results generated so far would prove problematic however. The animation tool is focused on applications involving changes of view and time. Given the needs and constraints of the solutions taken for this project, neither of these would be active components of this visualization, and would complicate the creation of the animation. Issues with preview playback, overlays and exports further complicated this. Given the relatively simple needs, a different approach using other software was selected.

In researching this topic, much forum discussion was found surrounding similar projects. Consensus seemed to be that for a visualization using static views such as this, exporting to an external main-stream video-processing platform would be most effective. To do this, each time view would need to be honed and exported as images through a layout. These layouts would then be arranged into a video with windows video editor.

Elements such as legend, title and attribution that had been causing issues under the animation tool were added to a layout. They automatically updated relevant information as layers were swapped within the layout view. Each layer in turn were exported as layouts representing each year range. Once these images were created, they were imported into windows video editor where they were composed into a timeline. Each layout was given period of 3 seconds before it would transition to the next layout. The video was then exported in 1080p and published to Youtube. Once hosted on Youtube, it can be easily embedded into a site like above or shared via link.

Fig. 5 – Video editing in Windows Video Editor

Future Work

There are different factors and semi-regular phenomenon that have impacts on tropical storm development. Events such as El Nino and the Pacific Decadal Oscillation are recurring events that could enhance. Relating the timeline of these events as well as ocean surface temperature could help interpret trends within this visualization. Creating a methodology behind time ranges displayed also could have enhanced this visualization. For example, breaking this visualization into phases of El Nino-Southern Oscillation rather than even time windows may have presented a lot of value to this sort of visualization.