The Challenger Glider Mission underwater-unmanned vehicle render (via rutgers)
It’s no secret that the world’s oceans play a big part in our planets weather. The sun heats the oceans surface, giving air pressure the energy it needs to produce storms and other weather patterns around the globe. Even though 70% of the Earth is covered in water and we can accurately predict what the weather will be for any given day, we still can’t get an accurate picture of our climate over long periods of time. Nor do scientists have a complete understanding of how exactly the oceans interact with the planet’s atmosphere. Sure, oceanic models have been created over the years but they are limited in scope and can show either large areas with reduced resolutions or small areas with high resolutions, which doesn’t give us a complete picture of our overall climate. This is needed to help accurately predict changes that affect agriculture, drought and even energy consumption.
To help gain those accurate models, researchers from Rutgers University and other institutions around the globe are looking to implement a fleet (16 in all) of underwater-unmanned vehicles to garner data on the Earth’s oceans in a project known as The Challenger Glider Mission. The fleet will travel at the forefront of deep ocean currents in five of the planet’s oceans and take data readings of water salinity, temperature, current speed/direction and even phytoplankton levels. The data will then be uploaded to the Iridium Satellite Network, where scientists can compare the data to current oceanic models in order to get an accurate high-resolution reading. Each of the 16 submersibles is fully autonomous and will use buoyancy changes in the waterfront to propel the drones, covering about 21 miles each day. Over the next two years, those drones will have covered nearly 80,000 square miles, navigating using onboard GPS, depth and altitude sensors housed in each of the drones. It’s the hopes of the researchers that by comparing the datasets, it will give them the edge in predicting weather patterns as well as fine-tuning climate-change models.
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