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Evaluating the Efficiency of Data Assimilation

07 January 2019

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Information is lost when researchers combine statistical models and remote sensing data, but just how much is often unclear. A new study offers a framework to measure the inefficiency.

The 20th century statistician George Box is widely credited with the remark that "essentially all [statistical] models are wrong, but some are useful." And it's true: As abstractions of the real world, models can only generalize the systems we study. The same holds for remotely sensed data, often collected via satellite or aircraft.

Source: EOS - Earth & Space Science News

Image credit: Dr. Amy McNally (NASA Goddard Space Flight Center) - A member of the SMAP team collecting soil moisture data for calibration and validation in Yanco, Australia.

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