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Originally Posted by XNN
More the former. For example, perhaps weather prediction models systematically fail to develop a certain kind of low pressure system rapidly enough. Scientists could then develop an observational program to intensively observe these events and then use the resulting data to improve the representation of the relevant processes in the model.
The latter is also a valid area of research. Models approximate the continuous natural world discretely, i.e., in finite time steps and at discrete grid points. As a simple example, if the time step is made too large, then the approximation may be poor, the model may be unstable, and it may never converge to a reasonable solution -- it "blows up". Improving the numerical stability of models, for example maintaining the same level of accuracy with a longer time step so that the model can be run faster, is definitely an active area of research.
Both, kind of... see above.
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Thanks, that does make the process clearer.
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Originally Posted by XNN
Our understanding and assumptions are, however, informed by empirical observations. When this fails to be the case, then problems can, as you've shown, definitely arise.
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Absolutely understanding and assumptions are informed by empirical observations, which is why modelling can be of benefit and can be accurate to a certain degree. However, as you agree, there are( or can be) inherent problems and as such modelling and simulations et al should be considered in light of that fact. That is not to say they should be thrown out altogether though.
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Originally Posted by XNN
I would say that the earth is more heavily observed now, and with a much more diverse set of observing platforms, than ever before, which is very encouraging and, from a scientific perspective, exciting.
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Agreed.
Cheers,
PKFFW