Posted on 10/11/2017 9:13:00 AM PDT by Oldeconomybuyer
A new statistical tool for modeling large climate and environmental datasets that has broad applications—from weather forecasting to flood warning and irrigation management—has been developed by researchers at KAUST.
Ying Sun and her PhD student Huang Huang developed a new method that uses a hierarchical low-rank approximation scheme to resolve the computational burden, providing an efficient tool for fitting Gaussian process models to datasets that contain large quantities of climate and environmental measurements.
The model was applied to a spatial dataset of two million soil-moisture measurements from the Mississippi River basin in the United States. They were able to fit a Gaussian process model to understand the spatial variability and predict values at unsampled locations. This led to a better understanding of hydrological processes, including runoff generation and drought development, and climate variability for the region.
"Our research provides a powerful tool for the statistical inference of large spatial data, says Sun. "And when exact computations are not possible, environmental scientists could use our methodology to handle large datasets instead of only analyzing subsamples. This makes it a practical and attractive technique for very large climate and environmental datasets."
(Excerpt) Read more at phys.org ...
First make some accurate predictions. THEN tell us how good it works.
Science uses the scientific method.
Climate “science” uses computer models.
Computer models do not use the scientific method.
Computer models are not science.
Furthermore, computer models cannot possibly include every climate variable, because the number of variables is infinite.
A computer model that does not include all the variables will diverge from the system it is ostensibly modelling.
Go ahead show us how you are modeling atmospheric water vapor .. Mr. phys.org ... and BTW the article was
Provided by: King Abdullah University of Science and Technology
Translation:
“Our Climate Model is now larger and more complex so it will be harder for you to tell that we’re cheating.”
OK, they’re thinking it’s better at figuring what’s in between different data points.
Sort of like interpolation. Wonder if those representations are converging series.. or not.
Oh well, thats math for someone else to do.
I use my own climate models plugged into my Oldeclimateoldeoscope.
Science can make use of computer models if they follow a real scientific method. Namely the computer model can help calculate a prediction based on a hypothesis. Then real life measurements can be compared to the prediction as a test of the hypothesis.
However, the climate "scientists" are not following this process at all. They are assuming the hypothesis is true, and then presenting the predictions of their computer models based on the hypothesis as if it were real life data.
What difference does the data make when they just “adjust” the raw data to suit their needs? Then if someone asks to see the data they just say “Why? You’re just trying to prove me wrong!”.
It can’t work. Read the book Nonlinear Oscillations, Dynamical Systems, and bifurcations of vector fields by Guckenheimer and Holmes. No matter how much data they have it won’t be enough. These guys are amateurs.
Might take me a few centuries to get to the book you suggest, I am still digesting Descartes...albeit his epistemology rather than his geometry. Also seems an expensive book, and I am guessing its not casual reading.
But from what little I do know combined with intuition, I am already highly dubious about the climate models getting it right. But I don't think its my burden to prove they can't do it. I think they ought to show it can be done. Not everybody can probably even understand the math much less take the time to do it.
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