A new report from Saildrone, one of the world’s leading collectors of ocean related in situ data on unmanned vehicles, says data collected by the wind and solar powered saildrones are validating information gathered via satellite remote sensing.
The report focuses on the 2019 NASA Multi-Sensor Improved Sea Surface Temperature Project mission in the Bering and Chukchi seas, the first of five years of temperature calibration and validation at high latitudes.
The Saildrone report quotes NASA’s Jorge Vazquez, the lead author of the study, who notes that he and his team found a strong correlation between measurements taken by satellite and measurements taken by the Saildrones in situ.
Vazquez presented his findings during last year’s fall meeting of AGU, a global community of earth and space scientists. He noted that one of the challenges in remote sensing is how to improve the satellite-derived data sets so people can use them for monitoring purposes. While satellite technology can offer estimates for sea surface temperatures, there’s still a question of how accurate those satellite estimates are, so Vazquez and his team deployed a number of Saildrones to collect similar data, finding a strong correlation between the two sets of measurements.
Data collected by the 2019 Saildrone fleet also is being used by Woods Hole Oceanographic Institution (WHOI) to develop machine learning tools to improve estimates of air-sea heat exchange in the Arctic Ocean and adjacent seas.
WHOI senior scientist Lisan Yu notes that Arctic surface air temperature is rising at twice the speed of the rest of the world and that sea ice in the Arctic is retreating up to three times faster than the rate projected by climate model simulations. Yu said the significant underestimate of Arctic sea ice loss in models underscores critical gaps in knowledge concerning interactions between the atmosphere, the polar ocean and ice-covered regions.
The Saildrone project, he said, brings all available observations together in a consistent and comprehensive way to help models make accurate predictions vital for development of effective policy responses to climate change.
