Twice a day, weather balloons are released into the atmosphere from 700 locations around the world to observe conditions in the upper atmosphere. Since the 1920s, there have been tens of millions of these radiosonde launches, producing an enormous archive of data that is critical to weather forecasting and climate modeling. In such a large data set, inevitable errors can significantly affect modeling outcomes.
Ying Sun, KAUST Assistant Professor of Applied Mathematics and Computational Science, collaborated with researchers from the Colorado School of Mines and Baylor University to develop a method to remove these errors using on a robust statistical analysis of the data.
“A radiosonde is a small, expendable instrument package that is suspended below a two-meter-wide balloon filled with hydrogen or helium,” explained Sun. “Sensors on the radiosonde measure height, pressure, temperature and dew point; they also calculate wind speed and direction by tracking the position of the radiosonde in flight. Radiosonde observations are the only direct measurements of the Earth’s upper atmosphere, making them vital for satellite data, weather forecasting and climatology research.
"There are far too many errors in the data to correct by hand, so we need an automatic method for identifying such random errors,” explained Sun.
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