Visualizing a winter storm
By Lindsay Lamb
This week brought us some very cold, very wintery weather here in Austin. We all spent a lot of time refreshing our various weather apps, checking the news, and listening to meteorologists’ weather reports on the radio. The reports shifted by the day, and sometimes even by the hour. I get it, this was an unprecedented weather storm which hasn’t been experienced in Austin in nearly 30 years. Many of us went without power, heat, water and food. Other than completely redoing the state of Texas’ power grid, offering some more detailed insights into the weather, power and water issues may have helped.
Let’s start with the weather. One thing that would have made knowing when the cold weather was coming, and what type of cold weather was likely to hit our area easier were error bars. Okay, okay, I know this wouldn’t work in a radio spot, but I think it could work in data visualizations on the news, and on apps or in a newspaper. Now, I’m not trying to upstage meteorologists (they have a hard job!) but perhaps some of their visualizations could use a little freshening up.
Here are some examples of what we typically see:
This is pretty a pretty typical experience that people have with navigating the weather. It is definitely informative, and you can click on the day and look at the forecast by hour, but there are still several unknowns. For example, it is always difficult interpreting (let alone explaining to an excited 2.5 and 6 year old) that a 35% chance of snow actually means there is a 35% chance that there will be snow in the greater Austin area, not that there is a 35% chance of snow at our house.
Here are some additional examples from the Weather Channel website and the Weather Underground website:
The Weather Underground’s website contains much richer information such as time of day when the highs and lows will peak (which is helpful in Texas when sometimes the daily high occurs first thing in the morning), and what the temperature actually feels like versus what the reported temperature is.
Now, wouldn’t it be nice to also see something that includes a range of values, and the likelihood of wintry conditions happening? Here are two simple ideas. One is a traditional box and whisker’s plot, the other is a simple line graph. Obviously, having lots of these wouldn’t be helpful, but for one day it would be super helpful to see that the low temperature could drop below freezing, so you should probably take steps to protect your pipes.
After I created these charts, I watched the news. I was happy to see that one weather reporter showed an image with sliding bars indicating the likelihood of freezing rain, snow or sleet. This was incredibly helpful! Sure, we were going to get some snow, but the thing we were most likely to get was freezing rain. Had he not showed these sliders, we just would have heard 90% chance of a wintry mix. Not helpful.
So, speaking of freezing rain, that is what caused power outages in my neighborhood. Several trees were downed by the heavy ice causing additional power outages. Now there is no way a data visualization could have helped us on the front end here. The only thing that could have helped would have been the city trimming trees near power lines and Austin Energy winterizing equipment. Where data visualizations did come in and help is on the back end. Here is The New York Time’s take on explaining what happened:
This visualization shows us when the winter storm hit, so we can clearly see what normal power generation was, and how all sources of energy plummeted due to the storm. This made a bad situation worse as this storm affected nearly every Texan.
Another great data visualization came from Austin water:
These data visualizations are helpful because they show how much water people normally use, and how the reservoir was nearly depleted. The other image is a heat map by zip code indicating where there were water issues as of 2/19/21.
The good news is that as of this writing (which took a big pause in the middle due to power outages and spending all of my energy keeping my family safe), most people have power, water and food. My daughter’s school survived the storm, and we will get back to pandemic life soon. I am thankful to all of our neighbors who came together to help out during this challenging time – in a pandemic no less! We came together to make sure people had warmth, water and food. In a time where we have been learning about resiliency, wants vs. needs, I am constantly amazed by how resilient my children – and all children – have been this year.