In the data-driven field of meteorology, scientists rely on the collection and intensive analysis of information to study weather. These methods enable recognition and prediction of weather patterns in order to provide people with accurate, up-to-the-minute forecasts. Today, high-performance computing (HPC) developers are leveraging Big Data to eliminate guesswork from the equation.
Meteorological observations from across the globe are uploaded to supercomputers and run against complex algorithms that predict what the weather will be in three to seven days. Thanks to advancements in HPC, cutting-edge software innovations are making it possible to capture and harness immense volumes of data, creating 3D visualizations in real-time and allowing researchers to communicate weather patterns, relative strength, and geographic occurrences. Yet weather visualizations are simplified, somewhat imprecise models of the real world. There is much more for researchers to learn from weather data to improve these models, making investments in Big Data technologies crucial to exploring, understanding, and accurately predicting weather phenomena.
When leveraged effectively, Big Data analysis takes on a significant role in the following:
- Monitoring hazards
- Determining human exposure to disaster risks
- Anticipating the impact of disasters
- Predicting future hazards
Data is becoming an increasingly valuable commodity as researchers strive to better understand weather and its associated risks. Kirk Borne, a professor and data scientist at George Mason University, confirms, “Big Data in weather first means that we have sensors everywhere: in space, looking down via remote sensing satellites, and on the ground” that provide continuous streams of information regarding weather, land use, vegetation, oceans, ice cover, precipitation, drought, water quality, and other variables. Researchers process staggering amounts of data every day from these sensors; for example, a single satellite launched by Japan in 2015 is providing 84 times the amount of data each day (approximately 42 gigabytes more) than Japan received before the launch.
Google Earth Engine is another entity exploiting the power of Big Data to model, analyze, and improve weather visualization. Their goal? To turbocharge scientific research using vast archives of satellite imagery and trillions of scientific measurements and to harness valuable data for public use. According to Earth Engine, the mapping algorithm required over one millions hours of computation; however, thanks to a network of supercomputers, Earth Engine was able to complete the project in a matter of days.
In order to manage the deluge of information, researchers are investing in transformative technologies such as HPC infrastructures and Big Data Server Storage Solutions to empower data-driven organizations. Channeling each byte into detailed visualizations that are up-to-date and easy to manage affords researchers the opportunity to closely monitor natural phenomena, forecast accurately, and lessen the impact of extreme weather.
Although it is impossible to control the weather, HPC and Big Data are significantly improving forecasting capabilities. Data visualization is key to studying weather in real-time, leveraging a greater knowledge of natural sciences, and raising awareness of hazards. Investing in these innovations is essential to effectively managing endless troves of information, and for visualization to evolve, Big Data is a must.