Be it is crazy hot Madrid summers or the ever-changing nature of British weather, the weather is a constant topic of conversation for people around the world. Historically, the weather is also something that has leveraged data. Record highs, record lows, and averages are frequently provided alongside daily weather forecasts. This data was all recorded and provided before the advent of Big Data. But how has Big Data changed the way weather is predicted?
On a personal level, Big Data is helping make weather prediction much more accurate and localized. Rather than having to check the weather for a large geographic area, companies like Dark Sky have developed apps that allow users to get weather forecasts specifically tailored to their exact neighborhoods. Or as Dark Sky puts it on their website, “With down-to-the-minute forecasts, you’ll know exactly when the rain will start or stop, right where you’re standing.” As Forbes highlighted in a recent article on different applications of Big Data, weather prediction is becoming more accurate because of the proliferation of sensors that can provide real-time weather updates. As the Internet of Things continues to expand, more and more everyday items, from fire hydrants to traffic lights, are equipped with smart technology that allows them to collect data and report back about conditions in their environment. This data can then be collated together with the available satellite weather data to provide incredibly accurate weather predictions.
Big Data is also being used to reduce the impact of extreme weather occurrences. For example, it allows forecasters to more accurately predict where and when massive storm systems will hit. As an interesting video from Datafloq highlights, when Hurricane Sandy hit the eastern coast of the United States in 2012, experts were able to predict landfall within 10 miles. This is contrasted with a 1990 storm prediction, which could target landfall within 345 miles – a rather large distance range! This level of accuracy allows people to prepare more efficiently and for any necessary evacuations to be more targeted. The financial implications of this increased accuracy are also significant. As the video points out, losses from extreme weather occurrences amount to over $200 billion annually and more than a third of global GDP is subject to impact from natural disasters.
|Figure 2: For major storms like Hurricane Sandy, landfall predictions are increasingly accurate.|
As climate change causes an increasing frequency of extreme weather occurrences such as Hurricane Sandy, climate scientists are calling on Big Data to join the fight. On the blog, we have previously looked at cases of how Big Data is being used to map the impact of climate change, but it is also being used to try to reduce that impact before it happens. Tools like Surging Seas, a project from the organization Climate Central, map and track rising sea levels around the world. This allows people to learn about their flood zone areas and prepare if they are at risk. The organization Cloud to Street focuses on the same topic to combine Big Data, flood plain info and demographic vulnerability stats in order to help those most at risk from devastating floods. As Cloud to Street describes it, this results in “Smarter planning, local resilience, and data empowered communities.”
|Figure 3: Organizations like Cloud to Street are helping those most vulnerable to climate change weather events.|
Whether it is making everyday life easier by telling people when they need an umbrella, or providing life-saving information about extreme storms, Big Data is changing what we know about weather. The elevated accuracy means that people can make better decisions around the weather, and this accuracy is only going to increase as more data is collected through the internet of things. At LUCA, we are excited to watch the development of how Big Data can be used to have both big and small impacts on how we interact with weather.