Combining nuanced statistical methods with a robust parallel computational platform has enabled a modeling scheme that better predicts environmental conditions while being efficient enough to cover millions of monitoring locations.
Combining nuanced statistical methods with a robust parallel computational platform has enabled a modeling scheme that better predicts environmental conditions while being efficient enough to cover millions of monitoring locations.
The new modeling approach developed by KAUST tackles a longstanding obstacle to improved weather and climate prediction: how to implement non-Gaussian statistics for very large geospatial datasets.
“In spatial statistics, the main objective is to use data observed at monitoring stations to predict the conditions at unobserved locations,” explains Sagnik Mondal, a Ph.D. student from Marc Genton’s statistics research group. “These types of predictions are necessary for many kinds of weather and climate applications. Nowadays, however, the number of observation locations can reach millions, which is beyond the capability of traditional computational approaches, and the traditional Gaussian models fail to statistically capture extreme values.”
Read more at: King Abdullah University of Science & Technology
A framework developed by KAUST statisticians enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally. (Photo Credit: © 2022 KAUST; Heno Hwang)
