I'd be curious to know what quantative information you use to track what seems to me to be scores of sources of model and
numerical errors in such a complex computer code as you are describing here.
I won't repeat what I said on an earlier forum, but a quick look at Paul Williams» presentation on
numerical errors in climate modeling shows a host of issues that would lead me to assign a rather high uncertainty to the model results, and then we have the uncertainties in the physical models themselves.
Its application to the C - grid reduces
numerical errors in the vertical mass flux resulting in improvements in precipitation and other quantities.
You may include, for example, the time you saved the company from losing a huge sum of money when you noticed
a numerical error in a report.
Not exact matches
New technique targets
numerical errors related to time evolution
in weather and climate models
The
error in a study that gets the
numerical calculations wrong can be easily identified and easily fixed.
There are no
numerical calculations that are
in error in the Old School SWR studies.
Anything you get is an emergent property of the physics involved (or
errors in your discrete analogue needed for making a
numerical model of the continuous equations).
Finite precision computer realizations of nonlinear models give unrealistic solutions because of deterministic chaos, a direct consequence of round - off
error growth
in iterative
numerical computations.»
If you use the computer models to tune parameters for the subgrid models, it becomes critical to control
numerical error or else the tuned parameters will only work
in the code you used with its time step and grids.
You need some way (and I think this may be the biggest issue
in climate models) of distinguishing
numerical errors and model
errors.
What is your response to the work of Paul Williams who has shown the critical importance of better
numerical methods
in climate models, not just for local
error (which everyone acknowledges is large) but for the time averaged properties and «patterns» that are claimed to be meaningful and repeatable.
TOAA is also relevant to reducing the large
errors associated with
numerical calculation
in climate models of the transfer of heat and moisture between ocean and atmosphere.
Among other reasons, small
errors in the
numerical modelling of complex processes have a nasty habit of accumulating with time.
In such systems, the tiny errors inherent in numerical prediction grow exponentially and soon render the solutions meaningles
In such systems, the tiny
errors inherent
in numerical prediction grow exponentially and soon render the solutions meaningles
in numerical prediction grow exponentially and soon render the solutions meaningless.
The IPCC then attempts to assert their models are robust and that therefore proves the CO2 hypothesis however, an objective look at the models would say that it seems unbelievable the models are robust on the face of them considering the computational complexity and
errors in the
numerical processes as well as the number of assumptions
in the underlying formulas.
It seems to me that there is always plenty of wiggle - room
in the alarmists calculations for them to produce whatever
numerical result is desired, with or without
error bars.
I think the
error in Kelly's derivation of the IPCC's formula for radiative forcing from CO2 which we were talking about was not merely
numerical, but was conceptual too.
The potential exists for spurious
numerical dispersion, when combined with
errors in parametrizations and incompletely modelled processes, to produce erroneous entropy sources.
Reviewing data for deficiencies or
errors,... to 12/2016 Senior Administrative Agent Iqor — Plymouth, MN Entered
numerical data into databases
in... taking on more responsibility with creative and administrative projects.